Nextworks (Italy)

The first outdoor field-trials of 3×1-distributed fiber-wireless mmWave antenna-system are experimentally presented, delivering uninterrupted real-time MEG-services at 2Gb/s user-rate and 0.195ms latency over an SDN-controlled coordinated multipoint X-haul with 7km fiber and 30m radio-distance. © 2025 The Author(s).

Background: Breast cancer is the most common female cancer in Italy. Despite better survival rates, significant disparities in access to diagnosis, treatment, and follow-up persist across regions. We propose an integrated, multidisciplinary care model-the Breast Immunology Network (BIN)-to address these challenges. Methods: The model was developed through a two-phase expert consultation with key opinion leaders and stakeholders, aligned with national and European oncology guidelines. No new patient data were collected; this is a qualitative analysis based on expert consensus and existing literature. The proposed model integrates a Hub-and-Spoke cancer network structure with fully functioning multidisciplinary teams (MDTs), standardized care pathways (PDTA), and digital tools to ensure continuity of care. Results: Experts identified critical gaps in Italy's breast cancer care: limited access to specialized centers, inconsistent adherence to screening programs, and delays in treatment initiation. The proposed BIN model aims to bridge these gaps by enhancing collaboration across all care levels, incorporating immunotherapy where appropriate, and defining key performance indicators (KPIs) for continuous quality evaluation. For example, quantitative targets include achieving ≥65% nationwide mammography screening adherence and ensuring ≥90% of patients are treated in certified Breast Units. Conclusions: The Breast Immunology Network offers a strategic framework to improve equity, quality, and timeliness of breast cancer care in Italy. Importantly, unlike existing Hub-Spoke or CCCN models, the BIN formalizes governance tools, harmonized eligibility criteria, and a national registry for immunotherapy. By uniting Breast Units and community services under shared governance, and by integrating innovations such as immunotherapy and telemedicine, the BIN model could significantly improve clinical outcomes and ensure more equitable care for all patients. Its implementation may serve as a reference model for other health systems seeking to optimize oncology pathways through multidisciplinary integration and advanced treatments.

The computing continuum aims to break the isolation of edge and cloud computing, creating a smooth and heterogeneous infrastructure surface for deploying applications. However, the continuum is practically fragmented, with infrastructure managed in isolation by various parties in a vertical dimension, i.e., edge, fog, and cloud layers, and horizontally, i.e., computing, network, and storage domains. This scenario negatively impacts the fulfillment of the application objectives. We propose inCoord, an intent-aware solution that enables the creation of a unified computing continuum by coordinating its instances to fulfill application objectives. Each instance represents a cluster of (compute, storage, or network) nodes with their own manager component. Traditionally, systems take low-level actions on these instances. In contrast, inCoord learns their emerging behaviors and adapts their managers’ objectives to fulfill the application’s intents. Here, through a Reinforcement-Learning-based Proof of Concept, we show the potential of this system to understand emerging behavior and manage multi-domain, independently managed instances.

Abstract Metal forming processes are significantly influenced by variations in sheet material properties and tool wear, which pose challenges to the repeatability of automated systems that rely on pre-set, rigid (i.e., non-adaptive) models. While skilled human operators can compensate for these variations by leveraging advanced visual and force feedback, automated solutions often lack real-time adaptability. Existing closed-loop control systems have demonstrated significant potential in addressing challenges such as variable springback. However, their high hardware costs have hindered widespread industrial adoption, particularly in adaptive bending applications. This study proposes a cost-effective hardware architecture that integrates a conventional predictive model with a 2D vision system and a collaborative robot (cobot) to overcome these barriers. By reducing the need for extensive material characterization, the system enables plug-and-produce functionality and ensures consistent output through real-time feedback and control, requiring minimal setup. Deployed in an industrial setting, the system evaluated multiple vision-based strategies, with ongoing research focusing on a marker-based, CAD-driven approach. Experimental results revealed an 80% reduction in processing time. Moreover, the proposed system achieved a final bend angle deviation of ±0.05°, significantly outperforming the 0.1°–0.5° error of human operators.

Fifth Generation (5G) networks and beyond are envisioned to provide user-focused communications, supporting diverse services with enhanced Quality of Service (QoS). Pivotal to this evolution is user equipment, which is increasingly performing advanced computational tasks beyond the edge of the network, known as the Extreme-Edge. Seamless integration of Extreme-Edge devices (EEDs) into the 5 G framework is however hindered, due to challenges in terms of device management, resource restrictions and interoperability issues. To address these barriers, we realize the Extreme-Edge Orchestrator (EEO), a management and orchestration framework enabling the extension of the 5 G cloud-to-edge continuum towards the Extreme-Edge. The EEO enables real-time resource monitoring and lifecycle management of network applications, including Artificial Intelligence/Machine Learning (AI/ML) tasks, deployed on EEDs. Unlike existing theoretical studies, our solution is deployed on an operational research-center-wide 5 G testbed and evaluated using an AI/ML-based network QoS prediction application, in the automotive domain. Our results show that the EEO supports efficient resource utilization, dynamic EED selection under device mobility scenarios and maintains robust service performance under computational stress-demonstrating its capability to support next-generation network services.

This paper presents the design and implementation of an Artificial Intelligence Control Plane (AICP) for deterministic networks, emphasizing the Proof-of-Concept (PoC) demonstration. The AICP framework integrates AI, digital twin technology, and real-time telemetry to manage complex network environments, ensuring reliable and low-latency communication. The PoC showcases the practical viability of the AICP by dynamically adapting to varying network demands and maintaining stringent to the Key Performance Indicator (KPI)s of the service. Extensive testing and real-world simulations highlight the framework's potential to enhance the efficiency and resilience of industrial communication networks.

With Industry 5.0, prioritizing worker well-being, the integration of human-related data into production processes poses challenges related to data privacy and compliance with regulations such as GDPR. This paper presents a comprehensive approach to anonymizing sensitive data collected from wearable devices. We propose a methodology that combines real-time processing and complex anonymization algorithms, ensuring end-to-end protection. We employ techniques such as normalization, hashing, and feature dropping. Our findings contribute to advancing privacy-preserving techniques in Industry 5.0 and underscore the importance of safeguarding user privacy in the era of advanced manufacturing.

Internet of Things (IoT) technology represents an important opportunity for future smart buildings. However, IoT systems require robust security measures to protect against threats that could compromise system functionalities, data privacy, and even the safety of inhabitants. To this end, this paper proposes a multilayer intrusion detection system (ML-IDS) that operates at both the network and sensor layers making use of a unified data engineering strategy for network and sensor data. The proposed solution is based on a general-purpose system that can accommodate new types of data available at network layer or other information generated by new IoT devices added to the system. To reduce the amount of data, the method introduces a data compression strategy based on sampling, aggregation, and image conversion, which allows the use of Convolutional Neural Networks (CNN). Performance analysis demonstrates the system effectiveness in identifying attacks at the network layer, achieving a False Negative Rate (FNR) of 0.16% and a False Positive Rate (FPR) of 4.76%, and at the IoT layer, with strong performance on four out of six types of sensors. Additionally, the data compression approach efficiently manages to convert data of different nature while reducing the validation dataset size from 20GB to 2MB.

TrialsNet is a project dedicated to enhancing European urban ecosystems through a variety of innovative use cases in domains including Security and Safety, Infrastructure, and Transportation. These use cases are being implemented across different clusters in Italy, Spain, Greece, and Romania, involving real users. This paper provides an overview of the diverse use cases, and of the corresponding network solutions, which leverage advanced functionalities like dynamic slicing management, NFV, MEC, AI/ML, and more. The project aims to identify network limitations, optimize infrastructure, and define new requirements for next-generation mobile networks. Ultimately, TrialsNet seeks to improve urban livability by driving advancements across multiple domains.

In the progressive development towards 6G, the ROBUST-6G initiative aims to provide fundamental contributions to developing data-driven, AIIML-based security solutions to meet the new concerns posed by the dynamic nature of forth-coming 6G services and networks in the future cyber-physical continuum. This aim has to be accompanied by the transversal objective of protecting AIIML systems from security attacks and ensuring the privacy of individuals whose data are used in AI-empowered systems. ROBUST-6G will essentially investigate the security and robustness of distributed intelligence, enhancing privacy and providing transparency by leveraging explainable AIIML (XAI). Another objective of ROBUST-6G is to promote green and sustainable AIIML methodologies to achieve energy efficiency in 6G network design. The vision of ROBUST-6G is to optimize the computation requirements and minimize the consumed energy while providing the necessary performance for AIIML-driven security functionalities; this will enable sustainable solutions across society while suppressing any adverse effects. This paper aims to initiate the discussion and to highlight the key goals and milestones of ROBUST-6G, which are important for investigation towards a trustworthy and secure vision for future 6G networks.