Mirabela-Melinda Medvei

Privacy-preserving machine learning in Vehicular Ad Hoc Networks (VANETs) must address sensitive on-board data, limited ground-truth annotations, and verifiable model-update integrity. This paper proposes ZK-FL, a decentralized federated learning framework for Intelligent Transportation Systems (ITS) scene classification that combines semi-supervised pseudo-labeling, zk-SNARK-based update validation, and Hyperledger Besu blockchain auditability. Vehicles train local models, quantize the resulting updates, and generate zero-knowledge proofs demonstrating that distributed update probes satisfy a predefined norm-boundedness constraint. Road-Side Units (RSUs) verify the proofs before aggregation and anchor the corresponding audit metadata on-chain. The framework is evaluated on the BDD100K and Mapillary Vistas v2 datasets under multiple non-IID data distributions and five aggregation methods: FedAvg, Norm Clipping, Median, Trimmed Mean, and Krum. Since Mapillary Vistas does not provide scene-level annotations, two scene-label generation strategies, including a semi-supervised pseudo-labeling approach, are introduced. Experimental results demonstrate effective scene classification, verifiable update integrity, and practical proof-generation and verification, supporting the feasibility of privacy-preserving federated learning for ITS.

Crime surveillance in urban environments faces increasing challenges due to dynamic conditions and the demand for real-time monitoring. This paper investigates the use of video data from autonomous vehicles to enhance situational awareness in public spaces through deep learning models optimized for edge processing. High-resolution vehicle-mounted cameras serve as mobile surveillance units capable of real-time object detection, human action recognition, and anomaly detection, bridging the gap between autonomous mobility and urban monitoring. Building on this vision, we introduce ITS-Vision, a generic framework that operationalizes these use cases, enabling autonomous vehicles to function as mobile, context-aware sensing platforms. To validate this approach, we develop prototypes for key ITS-Vision components: a fight detection module using a fine-tuned X3D model, suspect identification via MediaPipe for detection combined with FaceNet for embedding extraction, and a dangerous items detection module using a fine-tuned YOLOv11n model. Due to the limited availability of real-world autonomous vehicle video datasets, experiments were conducted in controlled laboratory environments, demonstrating the feasibility of the proposed architecture and algorithms under simulated conditions. Future work will focus on collecting dedicated datasets and advancing the models toward deployment in real urban scenarios.

Urban traffic congestion remains a major contributor to vehicle emissions and travel inefficiency, prompting the need for adaptive and intelligent traffic management systems. In response, we introduce DeepSIGNAL-ITS (Deep Learning Signal Intelligence for Adaptive Lights in Intelligent Transportation Systems), a unified framework that leverages real-time traffic perception and learning-based control to optimize signal timing and reduce congestion. The system integrates vehicle detection via the YOLOv8 architecture at roadside units (RSUs) and manages signal control using Proximal Policy Optimization (PPO), guided by global traffic indicators such as accumulated vehicle waiting time. Secure communication between RSUs and cloud infrastructure is ensured through Transport Layer Security (TLS)-encrypted data exchange. We validate the framework through extensive simulations in SUMO across diverse urban settings. Simulation results show an average 30.20% reduction in vehicle waiting time at signalized intersections compared to baseline fixed-time configurations derived from OpenStreetMap (OSM). Furthermore, emissions assessed via the HBEFA-based model in SUMO reveal measurable reductions across pollutant categories, underscoring the framework’s dual potential to improve both traffic efficiency and environmental sustainability in simulated urban environments.

The fast-paced adoption of smart devices has changed the Internet of Things (IoT) landscape, leading to the growth of smart environments and inclusion in many aspects of our society. In IoT applications, data collected from sensors and mobile devices are aggregated, processed, and analyzed to extract useful information and develop intelligent services. If the collected data is not trustworthy due to the damage or malicious input of some sensors, the quality of the service will be impacted. For reliable data collection and mining, it is mandatory to define robust security and trust models, suitable for the IoT application context. In this paper, we propose RESFIT, a platform that implements a reputation-based trust mechanism and an advanced application level firewall to cope with the above mentioned issues. Having a gateway-centric architecture, the proposed platform ensures minimal resource consumption at the node layer, and an integrated overview and control of the system state, through the cloud component and smartphone management application.