The open, virtualized, and disaggregated nature of Open Radio Access Network (O-RAN) designs has brought new security issues to mobile networks. Strong and flexible security measures are required in response to these issues, and the integration with existing IT security tools such as Security Information and Event Management (SIEM) systems may represent a viable solution. The method for combining SIEM with O-RAN is presented in this study in order to improve security threat detection and mitigation in mobile and IT settings. In order to improve real-time anomaly detection and reaction, the proposed system will design a specific xApp that acts as an agent to pass events from mobile networks to the SIEM. This would address threats like jamming and Distributed Denial of Service (DDoS) assaults. In addition to improving Intrusion Detection Systems' (IDS) capabilities, SIEM integration with O-RAN offers a dynamic and intelligent security architecture that can react quickly to threats that are always changing in both IT and mobile infrastructures. This study shows how O-RAN and SIEM technology can be combined to protect next-generation mobile networks against advanced cyberattacks.

This work introduces a standardized and lightweight abstraction framework for Deep Audio Embedding Neural Networks. Designed with edge and embedded scenarios in mind, the framework enables seamless integration, inference, and performance profiling of a large set of pre-trained Audio NNs, across diverse downstream domains and platforms, by introducing a unified API and an open-contributing configuration schema. This interface allows each model to be instantiated and executed through an easy interpretable descriptor, without altering the original developers source code. The proposed framework supports fully automated benchmarking orchestration in a cloud-based environment, with the aim of a modular infrastructure built on Amazon Web Services, which processes community-contributed model configurations and evaluates them on server-grade hardware. Benchmark profiling includes inference (CPU/GPU) timing, energy efficiency, and memory usage monitoring, minimizing runtime overhead and enabling fair comparison across implementations: results are aggregated into a real-time leaderboard and within technical model cards, accessible through a dedicated web interface. This initiative lays the groundwork for a community-driven benchmarking challenge, encouraging experimental reproducibility, scalability, and models cross-compatibility for real-world audio intelligence applications.

Ensuring seamless connectivity for first responders represents a critical issue in emergency scenarios, where disrupted infrastructures and network congestion can significantly hinder the communication. Open Radio Access Network (O-RAN) promises a level of openness in Radio Access Networks (RANs) that could be advantageous under such critical conditions, allowing for enhanced integration across different operators’ networks to mitigate resource scarcity. This paper introduces an architectural solution based on O-RAN that leverages neutral hosting to provide a unified gateway to RAN infrastructures. Our approach facilitates seamless access for emergency responders and optimizes the allocation of resources at both the RAN and Core levels within a specified area by exploiting the deployment of near-real-time Applications (xApps) and non-real-time Applications (rApps) managed by the neutral host. The results demonstrate the effectiveness of O-RAN neutral hosting in enhancing first responders’ connectivity, showcasing significant improvements in throughput and latency, especially under disaster scenarios. • O-RAN Neutral Hosting is a feasible solution for emergency operator connectivity. • First responders seamlessly communicate through efficient radio resource allocation. • O-RAN Neutral Hosting outperforms traditional RAN before and after the disaster. • Slice isolation strategy further amplifies the advantages of O-RAN Neutral Hosting.

The adoption of Time Division Duplexing (TDD) in 5G not only facilitates efficient spectrum utilization but also paves the way for the implementation of innovative schemes that leverage the unique characteristics of the TDD patterns. For example, Cooperative Dynamic Bandwidth Allocation (CO DBA) exploits information about the TDD pattern to reduce latency in Time Division Multiplexing Passive Optical Networks (TDM-PON)-based fronthaul. In this paper, we harness the information about 5G TDD patterns alongside the programmability offered by Open Radio Access Network (O-RAN) and Software Defined Optical Access Networks (SDOANs) for a different purpose: enhancing x-haul energy efficiency. The scheme we propose seeks to minimize the energy consumption of PON-based x-haul networks by dynamically adjusting the operational states of selected subsystems within the Optical Network Terminal (ONT) in coordination with the TDD patterns. Preliminary simulation results show that up to 80% energy savings can be achieved with the proposed cooperative technique.

The rapid evolution of next-generation mobile networks introduces challenges and opportunities in achieving various use cases with extremely low latency, high data rates, and dense user connectivity. However, these objectives can lead to the higher energy consumption of mobile networks, particularly within the Radio Access Network (RAN), which generally consumes 75% of the mobile networks total energy consumption. The current studies available focus mainly on the energy consumption of the next-generation Core Network (5GC), while this demonstration provides a better understanding of the energy consumption of the RAN components. The demonstration provides energy observability leveraging various open-source software tools to measure and monitor RAN energy consumption trends deployed on the Kubernetes platform — a widely adopted container orchestration platform. Moreover, this demonstration shows energy consumption on different RAN architectures — Monolithic, Disaggregated, and Control Plane and User Plane Separation (CUPS) — utilizing tools such as Kepler and Scaphandre for comprehensive energy measurement.

This demonstration shows the effectiveness of a hierarchical Software-Defined approach in coordinating Radio Access Network (RAN) and Passive Optical Network (PON)-based RAN transport to proactively scale virtual Distributed Units (vDUs) / Radio Units (RUs) and to jointly reconfigure the mid-haul transport.

With the advent of advanced communication technologies like 5G and the emergence of the Internet of Everything, there is a growing potential to leverage public communication networks for mission-critical applications. Traditionally, these applications have depended on private, narrowband networks such as TETRA and P25. The latest communication protocols and architectures promise specific levels of latency and reliability even during the negotiation phase. Concurrently, the increasing deployment of sensors for Structural Health Monitoring (SHM) offers access to vast data volumes at low costs and near real-time capabilities when required. This paper highlights the significant benefits of integrating these two paradigms in the context of Earthquake Early Warning (EEW) systems. Utilizing existing earthquake propagation models and recorded seismic data, we have developed a new simulator tailored to mimic a densely packed sensor network environment. We introduce a consensus algorithm that processes the initial milliseconds of data collected by sensors nearest to the earthquake’s hypocenter. An alert is then promptly issued. Our results indicate substantial improvements in reducing the time to relay warnings, thereby significantly diminishing the extent of the blind zone, where warnings arrive too late, in EEW systems.

Recently, container-based solutions have become de facto compute units of modern cloud-native applications. However, the exponential growth in data traffic and the power consumption of these technologies to handle high data traffic alarm the strong need for energy evaluation approaches in containerized clouds. Furthermore, the proliferation of highly distributed edge clouds raises additional concerns regarding the power consumption of future cloud architectures. This article presents a detailed overview of methods and techniques for monitoring power consumption within popular cloud platforms. The study offers an in-depth evaluation of these approaches, demonstrating variations in measured power consumption based on the chosen technique. A well-known container orchestration platform named Kubernetes (K8s) has been applied in our extensive measurements. This work argues that energy-efficient container clouds will play a vital role in building a more sustainable and eco-friendly digital infrastructure by optimizing power consumption and reducing carbon footprint, paving the way for a greener future. The paper also discusses open challenges and future research directions on energy sustainability, leading to the conclusion, offering lessons learned and prospects on potential solutions to foster sustainable practices within the container ecosystem.

The OPTIMIST project is positioned into one of the most challenging and societal impactful innovation paths, by contributing to the design and implementation of a Multi-access Edge Computing-empowered service platform enabling the optimized delivery of the most popular type of traffic in the forthcoming 5G networks: mobile video. Advanced prediction tools (e.g. data analytics), MEC-enabled service provisioning, 5G networking, QoE-driven and model-based service optimization using stochastic modeling and algorithmic optimization, are major components toward a holistic system that combines analytical tools and close-to-market technologies. In addition, despite the considerable amount of valuable academic research conducted in the area of MEC- enabled service provisioning, the vast majority of obtained results remains hardly compatible (in terms of system models and implementation platforms) and are often limited to the scope of academic research with no envisaged market transfer plans. OPTIMIST fosters fruitful collaboration of academia and industry targeting at developing an innovative platform for fully personalized delivery of 5G mobile video content.

Network and computing infrastructures are nowadays challenged to meet the increasingly stringent requirements of novel applications. One of the most critical aspect is optimizing the latency perceived by the end-user accessing the services. New network architectures offer a natural framework for the efficient orchestration of microservices. However, how to incorporate accurate latency metrics into orchestration decisions still represents an open challenge.In this work we propose a novel architectural approach to perform scheduling operations in Kubernetes environment. Existing approaches proposed the collection of network metrics, e.g. latency between nodes in the cluster, via purposely-built external measurement services deployed in the cluster. Compared to other approaches the proposed one: (i) collects performance metrics at the application layer instead of network layer; (ii) relies on latency measurements performed inside the service of interest instead of utilizing external measurement services; (iii) takes scheduling decisions based on effective end-user perceived latency instead of considering the latency between cluster nodes.We show the effectiveness of our approach by adopting an iterative discovery strategy able to dynamically determine which node operates with the lowest latency for the Kubernetes pod placement.

In this paper, we present the current prototype of the CrazySquare Project which is aimed to provide a gamified ICT (Information and Communications Technology) solution for musical education. The project is inspired by Gordon's Musical Learning Theory. It is dedicated to the guitar since it is one of the most played instrument in Italian's Middle Schools. The TPACK (Technological Pedagogical Content Knowledge) framework has been used as a way to effectively integrate the technology into teaching activities. Moreover, the CrazySquare project follows an iterative process based on the TEL-oriented UCD approach. Currently, after carrying out an expert-based evaluation with several domain-experts, we are designing the user-based evaluation phase which will conclude the second iteration.