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.
Ray-I Chang, Ting-Wei Hsu, Yu-Han Ke· Italian National Conference...· 0 citations
Connected vehicles generate video, sensor, and bulk data that must be uploaded, cached, and forwarded across edge and cloud resources under short contact durations and congested backhaul. This paper studies selected data-management functions of a Smart Offloading Proxy (SOP) for 5G Internet of Vehicles (IoV): deadline-constrained scheduling of uploads already accepted at the edge proxy, and a radio-quality allocation signal. Control-plane functions are described but not evaluated; no end-to-end architecture validation is claimed. For proxy-side forwarding, six bandwidth-scheduling policies are formalized and evaluated in NS-3 against a first-come-first-served serve-one baseline. With bursty arrivals, a 60 Mbps bottleneck, and a 30 s dwell deadline, the shortest-remaining-k equal-allocation policy (SRK-EQ) is the strongest of the six scheduling policies, completing 92.5 ± 4.6% over 20 seeds versus 75.2 ± 12.0% for the all-jobs baseline; the serve-one baseline attains higher completion (98.0 ± 2.4%) for homogeneous 10 MB jobs. Under a heterogeneous 1/10/50 MB workload, SRK-EQ delivers lower latency (median 3.15 s versus 19.84 s) with overlapping completion estimates and a large-job fairness trade-off. In the tested replays, the Long Short-Term Memory (LSTM)-assisted configuration shows lower video and sensor delay with 38–48% lower mean per-flow video throughput than the baseline—a configuration-level latency-versus-throughput trade-off; the LSTM-specific effect is not isolated.