arXiv subjects

Antonio Boiano

Publications and source records attributed to Antonio Boiano.

5 recordsLinked to original sources

arXiv2026

Zigbee vs. Matter over Thread: Understanding IoT Protocol Performance in Practice

The widespread adoption of the Internet of Things (IoT) has positioned smart homes as paradigmatic examples of distributed automation systems, where reliability, efficiency, and interoperability depend critically on the underlying communication protocol. Among the low-power wireless technologies available for this scenario, Zigbee and Matter over Thread have emerged as leading contenders. While Zigbee represents a mature, non-IP mesh networking solution, Matter over Thread introduces an IP-based architecture designed to unify device interoperability across different ecosystems. However, despite extensive documentation of their design principles, there is a lack of empirical, comparative performance data under realistic network conditions. This paper presents a comprehensive experimental comparison between the two protocols, conducted on a testbed built from commercially available hardware. The proposed methodology focuses on different key performance dimensions, such as scalability, responsiveness, and fault tolerance. The results reveal that Zigbee achieves a lower baseline overhead and faster route recovery, making it more responsive in static small-scale deployments. Matter over Thread, conversely, exhibits superior scalability and robustness, maintaining stable throughput and predictable latency across multi-hop scenarios. Overall, we highlight that Zigbee and Matter over Thread embody distinct trade-offs between agility, efficiency, and scalability.

cs.NI↗

arXiv2026

Analyzing Zigbee Traffic: Datasets, Classification and Storage Trade-offs

Zigbee is widely used in smart home environments due to its low power consumption and support for mesh networking, making it a relevant target for traffic-based IoT forensic analysis. However, existing studies often rely on limited datasets and fixed network configurations. In this paper, we analyze Zigbee network traffic from three complementary perspectives: data collection, traffic classification, and storage efficiency. We introduce ZIOTP2025, a publicly available dataset of Zigbee traffic collected from commercial smart home devices deployed under multiple network configurations and capturing realistic interaction scenarios. Using this dataset, we study two traffic classification tasks: device type classification and individual device identification, and evaluate their robustness under both intra-configuration and cross-configuration settings. Our results show that while high classification accuracy can be achieved under controlled conditions, performance degrades significantly when models are evaluated across different network configurations, particularly for fine-grained identification tasks. Finally, we investigate the trade-off between traffic storage requirements and classification accuracy. We show that lossy compression of traffic features through quantization can reduce storage requirements by approximately 4-5x compared to lossless storage of raw packet traces, while preserving near-lossless classification performance. Overall, our results highlight the need for topology-aware Zigbee traffic analysis and storage-efficient feature compression to enable robust and scalable IoT forensic systems.

cs.NI↗

arXiv2024

A Federated Learning Platform as a Service for Advancing Stroke Management in European Clinical Centers

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.

cs.CY↗

arXiv2024

A Secure and Trustworthy Network Architecture for Federated Learning Healthcare Applications

Federated Learning (FL) has emerged as a promising approach for privacy-preserving machine learning, particularly 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 architecture 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 protocols (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 proposing mitigation strategies to increase the trustworthiness level.

cs.AI↗

arXiv2023

IoTScent: Enhancing Forensic Capabilities in Internet of Things Gateways

The widespread deployment of Consumer Internet of Things devices in proximity to human activities makes them digital observers of our daily actions. This has led to a new field of digital forensics, known as IoT Forensics, where digital traces generated by IoT devices can serve as key evidence for forensic investigations. Thus, there is a need to develop tools that can efficiently acquire and store network traces from IoT ecosystems. This paper presents IoTScent, an open-source IoT forensic tool that enables IoT gateways and Home Automation platforms to perform IoT traffic capture and analysis. Unlike other works focusing on IP-based protocols, IoTScent is specifically designed to operate over IEEE 802.15.4-based traffic, which is the basis for many IoT-specific protocols such as Zigbee, 6LoWPAN and Thread. IoTScent offers live traffic capture and feature extraction capabilities, providing a framework for forensic data collection that simplifies the task of setting up a data collection pipeline, automating the data collection process, and providing ready-made features that can be used for forensic evidence extraction. This work provides a comprehensive description of the IoTScent tool, including a practical use case that demonstrates the use of the tool to perform device identification from Zigbee traffic. The study presented here significantly contributes to the ongoing research in IoT Forensics by addressing the challenges faced in the field and publicly releasing the IoTScent tool.

cs.CR↗