The article presents a comprehensive methodology for assessing the safety of highly automated vehicles (HAVs) based on operational data collected on public roads. The methodology includes a qualitative classification of safety violations according to three criteria: impact on functionality, extent of coverage by safety mechanisms, and frequency of occurrence. Based on the sum of these criteria, a criticality matrix is constructed, distinguishing four risk zones: critical (requiring immediate remediation), unacceptable (requiring planned remediation), tolerable, and acceptable. The quantitative assessment includes a system of key performance indicators (operational efficiency, number of accidents, detection and elimination of threats, and frequency of driver/operator interventions) and an integral safety indicator. The applicability of the methodology was demonstrated using operational data from a Level 3 HAV collected over 1413 km of automated driving on Russian highways. In this demonstration, most of the recorded failures were classified in the critical zone, which made it possible to prioritize system improvements. The proposed methodology can be used by developers for the objective assessment of safety and by regulators for developing reporting requirements. From the perspective of sensor and actuator networks, this work makes three contributions: (1) the multimodal sensor network of the HAV (4 cameras, 4 radars, GNSS RTK) is analyzed as a networked system with redundancy and overlapping fields of view; (2) the CAN bus is treated as an actuator network whose reliability is critical for safety; (3) it is shown that 10 of 17 recorded failures (59%) are related specifically to network equipment, making the monitoring of sensor and actuator networks a central element of the proposed methodology.
The convergence of artificial intelligence (AI) and advanced biosensing is creating new opportunities to advance clinical diagnostics from reactive treatment toward proactive and precision medicine. This review examines AI-enabled biosensors across four key clinical domains: (1) infectious diseases, focusing on AI-integrated microfluidic point-of-care (POC) testing; (2) cancer, emphasizing AI-assisted liquid biopsy and early cancer detection; (3) neurological disorders, highlighting continuous electrophysiological monitoring; and (4) metabolic diseases, focusing on wearable sensing for personalized care and complication prediction. In addition to these applications, this review discusses key challenges that may limit clinical translation, including data heterogeneity, model overfitting, limited generalizability, sensor variability under real-world conditions, algorithmic interpretability, and regulatory requirements. Explainable AI (XAI), edge computing, and decentralized sensing architectures represent potential approaches to addressing some of these challenges, although their effectiveness depends on the specific application and implementation context. Importantly, promising analytical performance in individual studies does not necessarily establish clinical utility or readiness for routine deployment. Independent validation, evaluation across diverse patient populations and clinical settings, standardized performance reporting, and consideration of implementation costs and regulatory requirements remain important for assessing the clinical applicability of AI-integrated biosensors.
As an innovative application of wireless sensor networks (WSNs), the Solar Insecticidal Lamp Internet of Things (SIL-IoTs) is widely deployed for green pest control. However, existing deployment strategies commonly assume homogeneous energy-harvesting capabilities among nodes and mandate full coverage, thereby overlooking the realities of uneven solar irradiation and budget constraints in complex 3D terrains. To address these issues, this paper investigates the SIL deployment problem using real DEM data. We first introduce a 3D coverage model alongside a terrain-based solar energy weight model, and formulate the task as a partial coverage optimization problem constrained by both energy harvesting and budget limitations. To solve this problem and avoid premature convergence, we propose an Improved Jumping Particle Swarm Optimization (IJPSO) algorithm. Experimental results on benchmark datasets and DEM-based 3D terrain simulations demonstrate that IJPSO outperforms six competing algorithms, achieving average relative percentage deviations (RPD) of 0.045% and 0.140% and average 3D deployment objective values of 62.27 and 84.37 under the 80% and 90% coverage constraints, respectively. These results demonstrate the effectiveness of IJPSO in reducing deployment costs while maintaining solution consistency.
The rapid growth of the Internet of Things (IoT) in safety–critical applications requires lightweight and robust security mechanisms for resource–constrained devices. Existing authentication and key agreement schemes, including that of Al-Hejri et al., suffer from critical weaknesses, such as state desynchronization, lack of sender-forward secrecy, non–atomic authenticated encryption, replay and device cloning vulnerabilities, and insufficient formal verification. This paper introduces FRACE–IoT, a lightweight, hardware–bound forward–secure, and cloning–resistant authenticated encryption scheme for heterogeneous four–tier IoT networks. The proposed approach combines Curve25519 cryptography, physical unclonable functions, authenticated ChaCha20–Poly1305 encryption, and HKDF–BLAKE3 key derivation within a single–pass key establishment architecture that achieves sender-forward secrecy and hardware-enforced protection against credential extraction. Security analysis and formal verification using the BAN logic and the AVISPA tool under the Dolev–Yao model confirm session key security, mutual belief, and robust resistance to major cyber-attacks. Experimental evaluation on real embedded hardware IoT boards, namely, FIT IoT–LAB M3 and Nordic nRF52840DK, demonstrates that FRACE–IoT achieves a reduced computation cost of 187.92 ms, communication overhead of 1344 bits, and a total energy consumption of 210.10 mJ, making it suitable for efficient and scalable IoT security deployment.
This study proposes an integrated framework for photovoltaic (PV) anomaly diagnosis combining unmanned aerial vehicle (UAV) radiometric thermography and red–green–blue (RGB) imagery, supervisory control and data acquisition (SCADA) telemetry, meteorological measurements, solar-shading modelling, automated defect detection, and ground-level verification. The methodology was evaluated on a 26.73 kWp rooftop PV installation comprising 81 modules using 77 producing days and 3543 valid 30 min observations. UAV thermography identified an open-circuited string and shading-induced hotspots, with peak hotspot temperatures reaching 109.9 °C and temperature differences exceeding 60 °C relative to the surrounding module surface. Radiometric analysis showed uniform temperature elevations of up to 5.3 K for the open-circuited string. To isolate maintenance effects from meteorological variability, 376 pre- and post-intervention intervals were matched according to irradiance, ambient temperature, relative humidity, and wind speed. The analysis yielded a mean power increase of 0.518 kW (95% confidence interval: 0.321–0.714 kW), corresponding to 28.3% relative to the matched pre-intervention mean. Two of the four anomaly classes considered occurred on the installation (one open-circuited string and 14 shading-induced hotspot regions). Within the limits of this single-installation case study, the framework enabled thermal anomaly detection, root-cause discrimination, and quantitative assessment of PV performance changes, supporting condition-based maintenance and operational decision-making.
