Marco De Santis

Hydrogen-powered vehicles, particularly those using internal combustion engines (H2-ICE), represent a strategic solution for reducing emissions in the transportation sector. However, optimizing their energy efficiency remains a challenge, mainly due to variations in individual driving behavior. Aggressive driving styles lead to suboptimal fuel consumption, highlighting the need for adaptive engine control systems. In this study, we introduce an innovative machine learning-based control module designed specifically for hydrogen-powered engines. We implement a new physics-based labeling strategy to classify driving behavior as ”aggressive” or ”cautious” using vehicle dynamics from a comprehensive real-world dataset. We systematically compared six supervised learning classifiers, including Support Vector Machines, Decision Trees, Random Forests, Gradient Boosting, K-Nearest Neighbors, and Logistic Regression, taking into account accuracy, latency, and interpretability, which are critical for embedded automotive systems. Random Forest and Gradient Boosting emerged as optimal candidates due to their superior performance metrics and balanced computational complexity. Our results provide concrete guidelines for the implementation of real-time, behavior-sensitive control systems that improve the efficiency and adaptability of hydrogen mobility, laying a solid foundation for future implementations on embedded hardware.

The incorporation of Driver Monitoring Systems (DMSs) in vehicles is fundamental to enhancing road safety by continuously assessing driver behavior and identifying signs of fatigue or distraction. However, as these technologies evolve, they also present considerable cybersecurity challenges. This research undertakes an extensive Threat Analysis and Risk Assessment (TARA) of DMSs, adhering to the ISO/SAE 21434 standard, to methodically detect and assess potential security threats. A total of 115 threats were recognized and classified into 95 low-risk, 16 medium-risk, and 4 high-risk scenarios, underscoring key vulnerabilities in data transmission, sensor reliability, and communication frameworks. To mitigate these risks, we suggest a range of countermeasures, including enhanced encryption techniques, stringent authentication protocols, and reinforced access control mechanisms, designed to strengthen the security posture of DMSs in practical applications. This study introduces a structured framework for evaluating and addressing cybersecurity threats in alignment with industry regulations, facilitating the dependable and safeguarded implementation of DMSs in future vehicle architectures while contributing to ongoing progress in automotive cybersecurity.

Context: The digitalization of the healthcare sector faces significant challenges due to the diverse representation of data and their distribution across various hospitals. Moreover, security is a key concern as healthcare-related data are subject to the legal obligations of GDPR and similar data protection legislations. Standardization efforts like HL7 have been implemented to enhance data interoperability. However, authentication still remains a critical issue, even significant challenges. Aim: This research aims to improve and strengthen the authentication process by introducing a novel architecture for decentralized authentication. Additionally, it proposes a new approach to decentralized data management, which is crucial for handling sensitive medical data efficiently. Methodology: The proposed architecture adopts a user-centric approach, utilizing Self-Sovereign Identity (SSI). It introduced a new non-fungible token (NFT) type called Soulbound token (SBT) in the medical context, which will facilitate user authentication across different hospitals, effectively creating a federation of interconnected institutions. Results: The implementation of the proposed architecture demonstrated a significant reduction in authentication time across multiple hospitals. The use of SBT ensured secure and seamless user authentication, enhancing overall system interoperability and data security. The decentralized approach also mitigated the risks associated with centralized authentication servers. Conclusion: The study successfully presents a novel decentralized authentication architecture for the healthcare domain, leveraging SSI and SBTs. This approach not only accelerates the authentication process but also enhances data security and interoperability among hospitals. Future research should explore the scalability of this architecture and its application in other sectors requiring stringent data security measures.