A scalable and privacy-aware framework for connected vehicle systems using smartphone-based multimodal sensing
Abstract
Connected Vehicle (CV) networks have emerged as key enablers of next-generation intelligent transportation systems, improving road safety and operational efficiency through real-time data exchange. However, large-scale adoption remains limited due to two fundamental challenges: 1) an unclear stakeholder value proposition and 2) persistent privacy concerns arising from continuous data collection. In parallel, conventional road condition monitoring methods are slow, expensive, and unable to provide continuous large-scale coverage. This work proposes a scalable and privacy-preserving framework that leverages smartphone-based sensing as a practical surrogate for connected vehicle data. Unlike infrastructure-dependent or single-modality approaches, the proposed method integrates widely available mobile devices with multimodal data sources to enable cost-effective, deployable large-scale road monitoring. This improves immediate real-world applicability while strengthening the value proposition needed for broader CV adoption. The research involves four main aspects. First, it has produced large-scale multimodal datasets combining smartphone inertial sensing, GPS, vision-based inputs, geographic information, and environmental conditions to capture diverse real-world driving scenarios. Second, it has used robust machine learning models to fuse heterogeneous data for accurate road anomaly detection under noisy and variable conditions. Third, it has proposed a layered privacy-preserving framework combining federated learning, contextual k-anonymity, and differential privacy to address non-IID vehicular data while ensuring strong privacy guarantees. Privacy implications are further analyzed using the IEEE Digital Privacy Model. Finally, the framework is validated through a cloud-based monitoring system and a sensing module designed to handle smartphone orientation variability in real-world deployments. In addition, the study identifies a critical gap in standardized metrics for evaluating privacy-preserving methods and proposes the need for a unified privacy score to enable systematic comparison. This research contributes a unified framework that combines scalable sensing, multimodal intelligence, and layered privacy mechanisms to balance detection performance, privacy preservation, and deployment feasibility. Overall, the proposed system establishes a scalable, privacy-aware, and deployable approach for road condition monitoring that supports intelligent transportation systems while addressing key barriers to connected vehicle adoption.
