Radio Access Networks (RANs) are critical infrastructures that mobile operators continuously upgrade to accommodate increasing data traffic demands, stricter performance requirements, and evolutions in radio technologies. RAN updates can affect carrier-level Key Performance Indicators (KPIs) that are the foundational input to data-driven models for network management. However, to date, no study has systematically examined the dynamics of RAN deployments, and little is known about the actual prevalence of RAN updates or their impact on Machine Learning (ML) models for network automation. This paper presents a first characterization of RAN updates in a nationwide operational infrastructure composed of over 500,000 carriers. A network-side vantage point lets us (i) investigate the type and frequency of RAN modifications, (ii) assess the impact of such changes on a primary KPI for network management, i.e., the traffic volume served by individual carriers, and (iii) verify the final effects on a classical downstream ML application, i.e., traffic prediction. Our results reveal that RAN updates take place with notable frequency, e.g., occurring every few days even in medium-sized cities. Also, they affect in a significant way the demands at a considerable fraction of pre-existing carriers, where they can curb the accuracy of ML traffic forecasting models.

The rollout of 5G represents a significant advancement in the telecommunications industry, offering the potential for markedly enhanced speeds, reduced latency, and improved connectivity. Considering these anticipated advantages, it is interesting to understand the progressive adoption of the new technology by operators and their subscribers. In this paper, we analyze the evolution and current operation of the nation-wide 5G network of Orange, a leading mobile operator in France. By inspecting longitudinal data about (i) the over-five-year-long development of the country-wide 5G radio access infrastructure and (ii) the last two years of 5G traffic demands, we unveil how the operator has planned the deployment of the 5G radio access and characterize the actual usage patterns of the available 5G infrastructure. We also investigate the recent introduction of a 5G Standalone (SA) commercial service and its adoption by the mobile subscribers. We show that by mid 2025, the 5G network under study has achieved substantial coverage of populated areas and the operator has very recently started adding capacity layers to its 5G radio access. However, our investigation reveals that such massive infrastructure deployment efforts are not matched by a commensurate adoption of the technology by the end users, as the 5G capacity -especially for SA- stays largely underutilized.

Federated Learning (FL) has emerged as a pivotal privacy-preserving machine learning paradigm, enabling collaborative model train across distributed data sources. A main issue, however, is the lack of comprehensive testing environments that can accurately emulate real-world conditions at a large scale, particularly the impact of network dynamics and device capabilities on FL algorithm performance. To this end, we introduce FederNet, a novel platform designed to facilitate the development and testing of FL algorithms with realistic network and device emulation. We show how the proposed system provides a versatile platform for researchers to evaluate the performance, robustness, and scalability of FL algorithms under diverse and configurable scenarios. We describe the FederNet architecture, detail its network and device emulation capabilities, and outline potential use cases that demonstrate its utility in advancing FL research. By bridging the gap between algorithmic development and practical deployment challenges, FederNet aims to accelerate the innovation and adoption of FL technologies.

• Scanned IPv4 to find 14386 MQTT brokers and captured 3.2 billion messages • Few brokers generate most traffic (“elephants”), while many handle little (“mice”). • Brokers show low load overall, with heavy-tailed traffic patterns. • Over 88% of messages use QoS 0, prioritizing low-latency over delivery guarantees. • Topics and payloads mostly follow best practices, but outliers reveal inefficiencies. The Message Queue Telemetry Transport (MQTT) protocol is widely used in Internet of Things (IoT) applications, offering a lightweight and efficient communication model for resource-constrained devices. Despite its increasing adoption in domains such as smart homes, industrial automation, and environmental monitoring, large-scale empirical studies on MQTT traffic are rare, and existing work often focuses on controlled experiments rather than natural, in-the-wild deployments. In this paper, we address this gap by analyzing MQTT traffic “in the wild”. We developed a measurement framework to scan the IPv4 address space, identifying 14,386 active brokers. We collected about 3.2 billion messages over two weeks, enabling an in-depth study of broker throughput, topic structures, payload composition, and QoS configurations. Our findings reveal that broker throughput is generally low, suggesting limited stress in real-world usage. Topic structures vary significantly, with some brokers using deep hierarchies, which may impact distributed deployments. Structured payloads (JSON, Strings) dominate MQTT traffic, presenting opportunities for broker-side optimizations. Furthermore, QoS 0 is overwhelmingly preferred, indicating a focus on low-latency communication over reliability guarantees. These insights contribute to a better understanding of MQTT traffic patterns, which can be leveraged for protocol optimizations, scalability strategies, and security considerations for future IoT deployments.

The rollout of 5G promises major improvements in speed, latency, and connectivity over previous-generation radio access technologies. Our study analyzes Orange's nationwide 5G network in France, combining longitudinal data on infrastructure deployment with data traffic patterns. Early results show that while 5G coverage has steadily expanded and is presently reaching the vast majority of the user population, adoption by mobile subscribers remains limited, leaving much of the new capacity underutilized.

The rapid adoption of Internet of Things (IoT) devices in smart environments has led to a new era for digital forensics. As IoT devices become increasingly prevalent in homes, cities, and workplaces, they serve as silent observers of everyday human activities. Recent studies have investigated how network traces from these devices could be leveraged to support forensic investigations. However, this approach requires large-scale data collection while ensuring the confidentiality, anonymity, and integrity of the collected traces. This work advances the field of IoT forensics by introducing Chain4ensic, a blockchain-based chain-of-custody (CoC) framework designed to preserve network traces extracted from IoT devices in a secure way. The proposed framework utilizes Ethereum smart contracts and edge computing to preserve the integrity and traceability of digital evidence. Additionally, the proposed system facilitates public notifications of data access once authorized by a court of justice. The architecture is developed as an opensource solution and evaluated in a smart home scenario, demonstrating its feasibility and effectiveness in real-life applications. The results indicate low resource usage, high throughput, and small gas consumption, making it a promising approach to tackle the challenges of storing sensitive data from participants in IoT forensic investigations.

The rapid evolution of artificial intelligence (AI) technologies holds transformative potential for the healthcare sector. In critical situations requiring immediate decision-making, healthcare professionals can leverage machine learning (ML) algorithms to prioritize and optimize treatment options, thereby reducing costs and improving patient outcomes. However, the sensitive nature of healthcare data presents significant challenges in terms of privacy and data ownership, hindering data availability and the development of robust algorithms. Federated Learning (FL) addresses these challenges by enabling collaborative training of ML models without the exchange of local data. This paper introduces a novel FL platform designed to support the configuration, monitoring, and management of FL processes. This platform operates on Platform-as-a-Service (PaaS) principles and utilizes the Message Queuing Telemetry Transport (MQTT) publish-subscribe protocol. Considering the production readiness and data sensitivity inherent in clinical environments, we emphasize the security of the proposed FL architecture, addressing potential threats and proposing mitigation strategies to enhance the platform's trustworthiness. The platform has been successfully tested in various operational environments using a publicly available dataset, highlighting its benefits and confirming its efficacy.

Federated Learning (FL) has emerged as a promising approach for privacy-preserving machine learning, particu-larly in sensitive domains such as healthcare. In this context, the TRUSTroke project aims to leverage FL to assist clinicians in ischemic stroke prediction. This paper provides an overview of the TRUSTroke FL network infrastructure. The proposed archi-tecture adopts a client-server model with a central Parameter Server (PS). We introduce a Docker-based design for the client nodes, offering a flexible solution for implementing FL processes in clinical settings. The impact of different communication pro-tocols (HTTP or MQTT) on FL network operation is analyzed, with MQTT selected for its suitability in FL scenarios. A control plane to support the main operations required by FL processes is also proposed. The paper concludes with an analysis of security aspects of the FL architecture, addressing potential threats and to increase trustworthiness.