Vehicular data management at scale: Architectural frameworks for cars as mobile data centers

Abstract

The emerging paradigm of modern vehicles as sophisticated mobile data centers generates unprecedented volumes of telemetry, sensor, and interaction data that require novel management approaches. The architectural framework addresses dual requirements of edge processing for latency-sensitive applications and cloud infrastructure for deeper analytics and model development. Vehicle-to-everything communication protocols integrate with software-defined networks and distributed ledger technologies to ensure secure, efficient data exchange across the ecosystem. Technical challenges including bandwidth constraints, data redundancy, and privacy regulations are primary motivators for solutions based on federated learning, optimized compression algorithms, and context-aware processing. Resilient vehicular data management necessitates a multi-layered approach balancing computational requirements across the edge-cloud continuum while maintaining robust security postures. These foundations enable scaling next-generation intelligent transportation systems were vehicles function as key nodes in broader smart city infrastructures.