Andrea Acquaviva

Embedded IoT platforms are frequently deployed in hostile or physically exposed environments, where compromise of the operating system is a realistic threat. In conventional deployments, OpenSSL executes entirely in user space, leaving cryptographic keys and intermediate material potentially exposed in the presence of a compromised OS or privileged malware. This work presents a portable OpenSSL engine that enables secure and transparent offloading of cryptographic operations to OP-TEE, a software stack leveraging a Trusted Execution Environment (TEE) to confine cryptographic key material and security-critical computations within secure-world memory without modifying existing applications or altering the OpenSSL EVP interface. The proposed engine uses a GlobalPlatform Client API implementation as an underlying communication layer and supports both Copy-Based (CB) data transfer and a Shared-Memory (SM) mode, enabling performance tuning under the resource constraints typical of embedded IoT platforms. An experimental evaluation on an NXP i.MX7 industrial gateway shows that secure-world execution introduces bounded overhead dominated by REE–TEE transitions and memory-management operations. For bandwidth-intensive primitives such as SHA-256, SM mode improves throughput by approximately 10–15% over CB transfers. For symmetric encryption algorithms such as AES-256-CBC, SM communication provides a substantial throughput improvement of approximately 40%, indicating that data-movement overhead plays a significant role on embedded platforms. In contrast, asymmetric primitives (e.g. RSA-1024) remain largely insensitive to transfer optimisations because they operate on small, fixed-size operands, making the overall execution time dominated by invocation overheads and internal big-number computations rather than data movement.

Illegal dumping poses serious risks to ecosystems and human health, requiring effective and timely monitoring strategies. Advances in uncrewed aerial vehicles (UAVs), photogrammetry, and deep learning (DL) have created new opportunities for detecting and characterizing waste objects over large areas. Within the framework of the EMERITUS Project, an EU Horizon Europe initiative supporting the fight against environmental crimes, this study evaluates the performance of pre-trained prompt-based multimodal (PBM) DL models integrated into ArcGIS Pro for object detection and segmentation. To test such models, UAV surveys were specially conducted at a semi-controlled test site in northern Italy, producing very high-resolution orthoimages and video frames populated with simulated waste objects such as tyres, barrels, and sand piles. Three PBM models (CLIPSeg, GroundingDINO, and TextSAM) were tested under varying hyperparameters and input conditions, including orthophotos at multiple resolutions and frames extracted from UAV-acquired videos. Results show that model performance is highly dependent on object type and imagery resolution. In contrast, within the limited ranges tested, hyperparameter tuning rarely produced significant improvements. The evaluation of the models was performed using low IoU to generalize across different types of detection models and to focus on the ability of detecting object. When evaluating the models with orthoimagery, CLIPSeg achieved the highest accuracy with F1 scores up to 0.88 for tyres, whereas barrels and ambiguous classes consistently underperformed. Video-derived (oblique) frames generally outperformed orthophotos, reflecting a closer match to model training perspectives. Despite the current limitations in performances highlighted by the tests, PBM models demonstrate strong potential for democratizing GeoAI (Geospatial Artificial Intelligence). These tools effectively enable non-expert users to employ zero-shot classification in UAV-based monitoring workflows targeting environmental crime.

Wireless sensor systems can collect and share a large amount of data for different kinds of applications, but are also vulnerable to cyberattacks. The impact of cyberattacks on systems’ confidentiality, integrity, and availability can be mitigated by using authentication procedures and cryptographic algorithms. Authentication passwords and cryptographic keys may be stored in a non-volatile memory, which may be easily tampered with. Alternately, Physical Unclonable Functions (PUFs) can be adopted. They generate a chip’s unique fingerprint, by exploiting the randomness of process parameters’ variations occurring during chip fabrication, thus constituting a more secure alternative to the adoption of non-volatile memories for password storage. PUF reliability is of primary concern to guarantee a system’s availability. In this paper, the reliability of a Static Random Access Memory (SRAM)-based PUF implemented by a standard 32 nm CMOS technology is investigated, as a function of different operating conditions, such as noise, power supply voltage, and temperature, and considering different values of transistor conduction threshold voltages. The achieved results will show that transistor threshold voltage and noise are the operating conditions mostly affecting PUF reliability, while the impact of temperature variations is lower, and that of power supply variations is negligible.

Autonomous drone swarms operating in potentially hostile environments and communicating over inherently insecure wireless channels require robust security architectures. While AI-based algorithms are effective in detecting communication anomalies and intrusions, their deployment in low-power environments like drones is challenging, and the integrity of AI-driven decisions can also be compromised. This paper discusses a comprehensive drone communication framework that enhances security by leveraging an efficient PMU design for minimally intrusive, real-time system tracing. Our approach improves system resilience by combining PMU data to (i) secure the integrity of AI-based decisions and (ii) detect intrusions through network profiling. The framework integrates the hardware Root-of-Trust of the target System-on-Chip to ensure integrity and privacy.

Structural Health Monitoring (SHM) is a critical task for ensuring the safety and reliability of civil infrastructures, typically realized on bridges and viaducts by means of vibration monitoring. In this paper, we propose for the first time the use of Transformer neural networks, with a Masked Auto-Encoder architecture, as Foundation Models for SHM. We demonstrate the ability of these models to learn generalizable representations from multiple large datasets through self-supervised pre-training, which, coupled with task-specific fine-tuning, allows them to outperform state-of-the-art traditional methods on diverse tasks, including Anomaly Detection (AD) and Traffic Load Estimation (TLE). We then extensively explore model size versus accuracy trade-offs and experiment with Knowledge Distillation (KD) to improve the performance of smaller Transformers, enabling their embedding directly into the SHM edge nodes. We showcase the effectiveness of our foundation models using data from three operational viaducts. For AD, we achieve a near-perfect 99.9% accuracy with a monitoring time span of just 15 windows. In contrast, a state-of-the-art method based on Principal Component Analysis (PCA) obtains its first good result (95.03% accuracy), only considering 120 windows. On two different TLE tasks, our models obtain state-of-the-art performance on multiple evaluation metrics (R² score, MAE% and MSE%). On the first benchmark, we achieve an R² score of 0.97 and 0.90 for light and heavy vehicle traffic, respectively, while the best previous approach (a Random Forest) stops at 0.91 and 0.84. On the second one, we achieve an R² score of 0.54 versus the 0.51 of the best competitor method, a Long-Short Term Memory network.

Physical Unclonable Functions (PUFs) are frequently exploited for security purposes. In particular, they are adopted to generate unique authentication passwords and cryptographic keys, with no need to store any secret information in non-volatile memories that can be easily attacked. In this paper, we present a novel PUF based structure, that can be implemented at low costs in microcontrollers and FPGAs, by exploiting the circuitry that is already present in their General Purpose Input-Output (GPIO) interface. The proposed PUF based scheme has been implemented on five different FPGA devices, in order to experimentally verify their correct operation under various realistic operating conditions. We will show that our proposed PUF based structure features a good uniqueness (of the 49.75%), a uniformity close to the ideal value of the 50%, an average steadiness of the 99.33%, and a maximum reliability of the 99.61%, for a temperature ranging from 5°C to 65°C.