This paper investigates dynamic Resource Unit (RU) allocation strategies for Wi-Fi 6 (IEEE 802.11ax) networks integrated with Time-Sensitive Networking (TSN), targeting the limitations of static RU scheduling under dynamic traffic conditions. We propose a dynamic RU allocation algorithm that maps TSN traffic classes to Wi-Fi 6 Quality of Service (QoS) mechanisms, including Enhanced Distributed Channel Access (EDCA) and aligns TSN control with Ethernet-based TSN domains. The proposed solution is evaluated using the ns-3 DetNetWiFi framework developed by fortiss, focusing on time-sensitive traffic. Simulation results demonstrate improved network efficiency with reductions in latency, jitter, and packet loss compared to static RU allocation schemes. These findings highlight the potential of dynamic RU allocation to support deterministic communication requirements in Wi-Fi 6-based TSN deployments and to enhance the reliability of hybrid industrial networks.

Containerized microservices are widely adopted for latency-sensitive and compute-intensive applications, with Kubernetes (K8s) as the dominant orchestration platform. However, automating the deployment and management of multi-service applications remains challenging, particularly in heterogeneous Edge-Cloud environments. This paper evaluates the CODECO toolkit, an open-source framework designed to enhance container orchestration across distributed infrastructures. We compare CODECO with baseline K8s workflows using three key performance indicators: deployment time, level of manual intervention, and runtime performance with resource utilization. Experiments across diverse hardware platforms (ARM, AMD, RPi) and K8s distributions, including lightweight variants such as k3s, demonstrate that CODECO substantially reduces manual effort while maintaining competitive performance and acceptable overhead. These results validate CODECO as an effective solution for Edge-Cloud orchestration and highlight its potential to improve the flexibility and intelligence of K8s-based deployments.

Military capability is increasingly determined by software. Yet defence platforms are procured on decade-long timescales, while the software and AI models they carry must evolve in days or hours. This paper calls this mismatch the lifecycle paradox, and argues it is the central problem Software-Defined Defence (SDD) must solve. SDD rests on three dimensions: software and systems engineering (design, procurement, certification), AI engineering (sovereignty and trust of learned components), and connectivity and infrastructure engineering (timely exchange of information among sensors, AI, and operators). The proposed path to resilient SDD starts from civilian technologies, addressed through a continuous, DevOps-style loop: model-based systems engineering and simulation-based testing front-load design and verification; tactical connectivity and low-power edge execution carry that design into contested operation; continuous compliance, assurance, and variability management run as cross-cutting concerns. This loop is sustainable given capabilities already proven in automotive, manufacturing, space, and energy. The next step is validating them under adversarial or defence-certified conditions, with short-, medium-, and long-term paths to closing gaps. Closing the SDD gap while preserving civic benefits is a distributed responsibility: researchers must redirect methods toward adversarial conditions; industry must expose tooling to operational needs; policymakers must shape regulatory instruments; and defence agencies must validate results with operators. Recommendations span three horizons: a short-term baseline of adversarial testing and connectivity pilots; a medium-term pipeline of incremental certification; and a long-term validation closing the loop under operational conditions.

Validating network configurations and testing failure scenarios in IoT-edge-cloud environments without disrupting live infrastructure remains an open operational challenge. This paper presents a low-cost, fully open-source Network Digital Twin (NDT) for IIoT edge deployments, built on Containerlab, Open vSwitch, ONOS, and a Prometheus+Grafana observability stack. The framework integrates container-native topology emulation, SDN-driven traffic engineering, and real-time telemetry in a single deployable artefact. Validation against a physical Raspberry Pi edge WLAN shows strong distributional convergence on RTT median (delta = 0.4 ms) and UDP throughput (delta = 0.03 Mbps). Remaining divergences on TCP throughput and packet loss are attributed to identifiable virtualisation artefacts, with root causes and remediation paths provided.

This paper presents CODECO, a federated orchestration framework for Kubernetes that addresses the limitations of cloud-centric deployment. CODECO adopts a data-compute-network co-orchestration approach to support heterogeneous infrastructures, mobility, and multi-provider operation. CODECO extends Kubernetes with semantic application models, partition-based federation, and AI-assisted decision support, enabling context-aware placement and adaptive management of applications and their micro-services across federated environments. A hybrid governance model combines centralized policy enforcement with decentralized execution and learning to preserve global coherence while supporting far Edge autonomy. The paper describes the architecture and core components of CODECO, outlines representative orchestration workflows, and introduces a software-based experimentation framework for reproducible evaluation in federated Edge-Cloud infrastructure environments.

As a leading research institute in software-intensive systems, fortiss is actively shaping the vision of Sixth Generation Mobile Communication (6G). Our mission is to ensure that 6G technologies go beyond technical advancements and are aligned with societal needs. fortiss plays a key role in 6G initiatives worldwide, including contributions to standardization bodies and collaborative Research and Development programs. We focus on software-defined, AI-enabled, and sustainable communication services that prioritize human values and long-term impact. 6G will redefine digital connectivity through cognitive intelligence, decentralized orchestration, and sustainability-oriented architectures. As expectations rise for ultra-reliable low-latency communication (URLLC) and personalized digital services, 6G must outperform prior generations. It will rely on AI-native networking, Edge-Cloud resource orchestration, and energy-aware data frameworks, ensuring both technical performance and societal relevance. This white paper presents the fortiss vision for a human-centric, sustainable, and AI-integrated 6G network. It outlines key research domains such as semantic communication, green orchestration, and distributed AI, all linked to societal and technological challenges. The white paper is aimed at researchers, industry experts, policymakers, and developers. It articulates the strategic direction and contributions of fortiss to 6G, emphasizing responsible innovation and interdisciplinary collaboration toward a meaningful 2030 vision.

Artificial Intelligence (AI) is rapidly becoming a foundational layer of social, economic, and cognitive infrastructure. At the same time, the training and large-scale deployment of AI systems rely on finite and unevenly distributed energy, networking, and computational resources. This tension exposes a largely unexamined problem in current AI governance: while expanding access to AI is essential for social inclusion and equal opportunity, unconstrained growth in AI use risks unsustainable resource consumption, whereas restricting access threatens to entrench inequality and undermine basic rights. This paper argues that access to AI outputs largely derived from publicly produced knowledge should not be treated solely as a commercial service, but as a fundamental civil interest requiring explicit protection. We show that existing regulatory frameworks largely ignore the coupling between equitable access and resource constraints, leaving critical questions of fairness, sustainability, and long-term societal impact unresolved. To address this gap, we propose recognizing access to AI as an Intergenerational Civil Right, establishing a legal and ethical framework that simultaneously safeguards present-day inclusion and the rights of future generations. Beyond normative analysis, we explore how this principle can be technically realized. Drawing on emerging paradigms in IoT–Edge–Cloud computing, decentralized inference, and energy-aware networking, we outline technological trajectories and a strawman architecture for AI Delivery Networks that support equitable access under strict resource constraints. By framing AI as a shared social infrastructure rather than a discretionary market commodity, this work connects governance principles with concrete system design choices, offering a pathway toward AI deployment that is both socially just and environmentally sustainable.

Augmented reality systems in dynamic environments still struggle with the challenge of what information should be displayed at which time. This work focuses on the case of Mobile Pervasive Augmented Reality Systems (MPARS) and their use in dynamic environments such as outdoor sports. An open-source proof-of-concept for a machine learning-based architecture to implement an MPARS on a specific use case of outdoor usage in a sports environment is presented. The new design for the system relies on heuristics that combine technology acceptance indicators, sensing, and information volume criteria to show the user a contextually meaningful subset of information. The information to the user is displayed in close-to-real-time, and the system can adjust and customise to prevent information overload. A first set of experiments was carried out based on end-user preferences to show the feasibility of the proposed system. To provide meaningful feedback, i.e., the right information when needed or wanted, to sports users on their MPARS experience, a predictive model was trained and shown to be able to estimate when information should be displayed to the user.

This paper explores the role of energy-awareness strategies into the deployment of applications across heterogeneous Edge-Cloud infrastructures. It proposes methods to inject into existing scheduling approaches energy metrics at a computational and network level, to optimize resource allocation and reduce energy consumption. The pro- posed approach is experimentally evaluated using a real-world testbed based on ARM devices, comparing energy consumption and workload distribution against standard Kubernetes scheduling. Results demon- strate consistent improvements in energy efficiency, particularly under high-load scenarios, highlighting the potential of incorporating energy- awareness into orchestration processes for more sustainable cloud- native computing.

This perspective paper introduces a novel framework for container orchestration called CODECO. The CODECO orchestration framework relies on a data-network-computing approach to define the best infrastructure that can support the operation of next-generation Internet applications across a mobile, heterogeneous Edge-Cloud continuum. The selection of such an infrastructure is aligned with target performance profiles defined by the user, such as resilience or greenness. CODECO proposes to rely on decentralized Artificial Intelligence approaches to provide the most suitable infrastructure to an application deployment, considering infrastructural challenges, such as intermittent connectivity and node failure. This paper explains the current CODECO framework and gives insight into operational use-cases where CODECO is being deployed, as relevant examples of application for such a framework. Recent developments in the creation of the open-source CODECO framework are described and explained, allowing the use of the framework by the research community. The paper then provides a thorough analysis of CODECO's features in comparison with existing orchestration frameworks, explaining the benefits introduced with this dynamic orchestration approach.

Container orchestration handles the semi-automated management of applications across Edge-Cloud, providing features such as autoscaling, high availability, and portability. Having been developed for Cloud-based applications, container orchestration faces challenges in the context of decentralized Edge-Cloud environments, requiring a higher degree of adaptability in the verge of mobility, heterogeneous networks, and constrained devices. In this context, this perspective paper aims at igniting discussion on the aspects that a dynamic orchestration approach should integrate to support an elastic orchestration of containerized applications. The motivation for the provided perspective focuses on proposing directions to better support challenges faced by next-generation IoT services, such as mobility or privacy preservation, advocating the use of context awareness and a cognitive, cross-layer approach to container orchestration to be able to provide adequate support to next-generation services. A proof of concept (available open source software) of the discussed concept has been implemented in a testbed composed of embedded devices.

Aguilera, Past Secretary, and now Treasurer of the Chapter, highlighted the main activities and achievement of the past term, off ering mature refl ections on the past experiences and ongoing challenges for the Chapter.On the other hand, Prof. Miguel Gutiérrez Gaitán, presented his plans, vision, and main ideas about the future of the Chapter, with focus on the development of internationalization and student branches.Among the planned activities for the present term, the IEEE ComSoc Chile Chapter is glad to announce the technical sponsorship for the 2023 South American Conference on Visible Light Communications (SACVLC), to be held at the Universidad of Santiago de Chile (USACH), between November 8 and 10, 2023.Moreover, the IEEE ComSoc Chile Chapter is enthusiastic on participating in the 2023 ComSoc Sister Chapters Program Project, led by Prof. Mohab Mangoud and Prof. Ricardo Veiga, which will greatly benefi t IEEE student branches worldwide.Lastly, also in the line of the internationalization plan, the whole new ExCom of IEEE ComSoc Chile Chapter is joining eff orts in bringing to Chile, two IEEE ComSoc Distinguished Lecture Tours (DLTs) among diff erent academic institutions, including UNAB, UDP, and USACH, among others.To conclude, the IEEE Chile ComSoc Chapter invite all the local, regional, and international community to contact the new Chair,

Interoperability remains to be one of the main challenges in the Internet of Things. The increasing number of IoT data sources from various vendors augments the complexity of integrating different sensors and actuators on the existing platforms, requiring human involvement and becoming error prone. To improve this situation, devices are usually coupled with a semantic description of their attributes. Such semantic descriptions, Things Descriptions, TD, are therefore an abstraction of devices, that is helpful to achieve a smoother integration of devices into IoT platforms. However, TD are usually vendor-based, so for large-scale IoT infrastructures, the integration complexity increases, as there will be different descriptions of similar sensors, provided by different vendors to be interconnected into IoT platforms. In this context, the paper assesses different ML-based semantic matchmaking approaches, against a sentence-based statistical similarity approach. For the ML approaches, the paper focuses on clustering and Natural Language Processing. The three approaches have been implemented on a realistic testbed, and experiments carried out show that the best performance achieved in terms of accuracy, time to completion of a matchmaking request, and memory usage is the NLP-based approach.