In-Vehicle Time-Sensitive Networking with Blockchain-Based Error-Bounded Data Management

Abstract

Autonomous driving systems (ADSs) increasingly rely on LiDAR sensors for perception. However, the resulting high-volume data places a strain on storage systems and network bandwidth and raises data-privacy concerns. We propose an IoT data engineering framework for processing, transmitting, storing, and retrieving high-volume LiDAR sensor data in in-vehicle systems that combines error-bounded compression and blockchain-based storage over in-vehicle Time-Sensitive Networking (TSN). With IEEE 802.1Qbv-based TSN scheduling, our framework supports deterministic delivery within the evaluated setup. It combines AES-GCM encryption, blockchain smart contracts, and InterPlanetary File System (IPFS) storage to support confidential, tamper-evident archival under the stated trust and threat model. Experimental evaluation on the KITTI dataset demonstrates that our BEDM framework reduces LiDAR data volume by 75.4%, contributing to a total network bandwidth reduction of 53.7%. The results demonstrate the feasibility and effectiveness of the integrated framework within the evaluated KITTI-based setup and single-switch TSN abstraction, and cross-scene and TSN traffic-sensitivity analyses further characterize its robustness.