With the surging demand for data-intensive applications, ensuring seamless content delivery in Satellite-Terrestrial Integrated Networks (STINs) is crucial, especially for remote users. Dynamic Ad Insertion (DAI) enhances monetization and user experience, while Mobile Edge Computing (MEC) in STINs enables distributed content caching and ad insertion. However, satellite mobility and time-varying topologies cause service disruptions, while excessive or poorly placed ads risk user disengagement, impacting revenue. This paper proposes a novel framework that jointly addresses three challenges: (i) service continuity-and topology-aware content caching to adapt to STIN dynamics, (ii) Distributed DAI (D-DAI) that minimizes feeder link load and storage overhead by avoiding redundant ad-variant content storage through distributed ad stitching, and (iii) revenue-aware content distribution that explicitly models user disengagement due to ad overload to balance monetization and user satisfaction. We formulate the problem as two hierarchical Integer Linear Programming (ILP) optimizations: one content caching that aims to maximize cache hit rate and another optimizing content distribution with DAI to maximize revenue, minimize end-user costs, and enhance user experience. We develop greedy algorithms for fast initialization and a Binary Particle Swarm Optimization (BPSO)–based strategy for enhanced performance. Simulation results demonstrate that the proposed approach achieves over a 4.5% increase in revenue and reduces cache retrieval delay by more than 39% compared to the benchmark algorithms.

Quantum key distribution (QKD) offers exceptionally high levels of data security during transmission by using principles of quantum physics. It is renowned for its provable security features. However, a gap between theoretical models and real-world applications, known as quantum hacking, challenges the reliability of QKD networks. Trojan-horse attacks represent a significant threat to the Bob subsystem in QKD, allowing Eve to infer Alice’s basis choices through back-reflected pulses. This can compromise security without detection in severe cases, especially when quantum bit error rates (QBER) fall below the abort threshold. The proposed method combines a category-based Gaussian Mixture Model (GMM) with the Kolmogorov-Smirnov test to estimate the posterior QBER distribution and assess risks in practical QKD systems. By processing the QBER, the approach also evaluates the dependability of the QKD scenario. Numerical results are presented using a state-of-the-art point-to-point QKD device operating over optical quantum channels of 1 m, 1 km, and 30 km lengths. The results of the experimental analysis of a 30 km optical link suggest that the QKD device provided prior information to the proposed learner. Consequently, our proposed trustworthy monitor offers a defensive mechanism that identifies potential Eve attacks, effectively mitigating the risk of security vulnerabilities.

Network slicing (NS) is one of the key techniques to manage logical and functionally separated networks on a common infrastructure, in a dynamic manner. As the complexity of virtualizing a full infrastructure required unprecedented effort, the initial idea of combining satellite and terrestrial networks has not been fully implemented in 5G yet. 6G networks are expected to further bring NS to a substrate network that is more heterogeneous, due to the full integration between terrestrial and satellite networks. NS describes the process of accommodating virtual networks, typically composed of nodes and links with the respective requirements, into the main infrastructure. This is an NP-Hard problem, typically also known as Virtual Network Embedding (VNE). Existing VNE solutions are designed per use-case and lack flexibility, adaptation and traffic-awareness, especially in such dynamic satellite environment. In this work, we investigate the VNE implementation to integrated satellite-terrestrial networks and propose a novel flexible framework, named Slice-Aware VNE for Satellite-Terrestrial (SAST-VNE), which 1) operates based on traffic prioritization; 2) jointly optimizes the load-balancing and the migration cost when network congestion occurs; and 3) provides a near-optimal solution. We compare SAST-VNE to existing well-known near-optimal VNE algorithms such as VINEYard and CEVNE and the shortest-path SN-VNE solution for satellite networks. The simulations showed that SAST-VNE reduces the migration costs between 10% and 40% during satellite handovers while maintaining the network load under control. Furthermore, when congestion occurs, SAST-VNE proved to be flexible in matching the priority of the slice, i.e., tolerated latency, with the time complexity and optimality of the solution.

During natural disasters such as earthquakes, wildfires, hurricanes, landslides, tsunamis, CBRNE (chemical, medical, radiological, nuclear, or explosive) incidents, or terrorist threats, critical infrastructure can be severely damaged, with rescue workers facing immense obstacles. Providing help and reaching affected areas can be hazardous for human rescue personnel. To overcome this issue, 5G-enabled UAVs can play a significant role in deploying disaster-resilient networks. Such networks imply multiple challenges, including end-to-end communication reliability and low latency for UAV real-time control. In this paper, we focus on the end-to-end communication aspects of such networks. We present and implement our disaster-resilient network based on a combination of 5G-UAVs and satellite networks. We highlight the related challenges and define solutions based on 5G, Multi-access Edge Computing, UAVs, and dynamic geofencing. We integrate these solutions in an end-to-end network architecture and ultimately deploy it on a national joint 5G-satellite infrastructure to assess its performance. It performs well and demonstrates a recovery time of less than 30 seconds with $\mathbf{9 9 \%}$ network availability.

Satellite Edge Computing (SEC) is seen as a promising solution for deploying network functions in orbit to provide ubiquitous services with low latency and bandwidth. Software Defined Networks (SDN) and Network Function Virtualization (NFV) enable SEC to manage and deploy services more flexibly. In this paper, we study a dynamic and topology-aware VNF mapping and scheduling strategy within an SDN/NFV-enabled SEC infrastructure. Our focus is on meeting the stringent requirements of mission-critical (MC) applications, recognizing their significance in both satellite-to-satellite and edge-to-satellite communications while ensuring service delay margin fairness across various time-sensitive service requests. We formulate the VNF mapping and scheduling problem as an Integer Nonlinear Programming problem (INLP), with the objective ofminimaxfairness among specified requests while considering dynamic satellite network topology, traffic, and resource constraints. We then propose two algorithms for solving theINLPproblem: Fairness-Aware Greedy Algorithm for Dynamic VNF Mapping and Scheduling (FAGD_MASC) and Fairness-Aware Simulated Annealing-Based Algorithm for Dynamic VNF Mapping and Scheduling (FASD_MASC) which are suitable for low and high service arrival rates, respectively. Our extensive simulations demonstrate that bothFAGD_MASCandFASD_MASCapproaches are very close to the optimization-based solution and outperform the benchmark solution in terms of service acceptance rates.

Before the advent of High-Throughput Satellites (HTSs), the satellite capacity was not enough to accommodate a large amount of data. Thanks to HTS, beyond 5G networks will boost the cooperation between space, air and terrestrial networks. The coexistence of heterogeneous QoS traffic demands, (e.g., emergency services, In-Flight Connectivity (IFC), Earth Observation Missions (EOMs)), over a dynamic environment, such as Multi-layer satellite-terrestrial networks, made the routing complex to handle. Additionally, due to numerous Inter-Satellite Links (ISLs) and their frequent changes, a testbed with real network emulation is challenging to develop.It is relevant not only to optimize the dynamic routing, but also to emulate the network in a testbed. This allows to consider systems constraints such as communication and technology delays in the most realistic manner. This paper investigates the future coexistence of the mentioned use cases for integrated Non-Terrestrial Networks (NTN)-terrestrial networks. We use Software Defined Networking (SDN) to monitor the substrate network with traffic statistics and apply routing decisions, via traffic handovers, during unexpected situations (congestion, link unavailability), in a reactive manner. Furthermore, we show that, for traditional handovers due to loss of Line of Sight (LoS), the SDN controller manages the procedure proactively to minimize traffic losses.

With the recent integration of Non Terrestrial Networks (NTNs) into 3GPP Release 17, 5G networks are expected to benefit from the NTN large coverage area. This integration will help mobile terrestrial networks reach a worldwide coverage. However, this ultimate ubiquity also comes with its set of challenges to overcome. One of the main issues is the seamless integration of NTNs into the existing mobile network standard. In this paper, we propose a comprehensive architecture integrating NTNs as slice-aware backhaul links. This architecture remains fully compliant with the 3GPP standard. For this purpose, we propose an end-to-end slice model integrating NTNs and 5G networks. Then, we implement this model on a 5G-satellite testbed, adding new functional components to interconnect both networks at the control and data plane levels. Lastly, we evaluate the performances of our method using the aforementioned testbed by monitoring each slice and their related Quality of Service requirements.