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Bringing the Clinic Home: An At-Home Multi-Modal Data Collection Ecosystem to Support Adaptive Deep Brain Stimulation

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

Ray-I Chang¹, Ting-Wei Hsu¹, Yu-Han Ke¹

¹Department of Engineering Science and Ocean Engineering, National Taiwan University, Taipei 10617, Taiwan.

Sensors (Basel, Switzerland)

|July 15, 2026

Summary

Autonomous driving systems generate massive LiDAR data. Our framework uses error-bounded compression and blockchain storage over Time-Sensitive Networking (TSN) to reduce data volume by 75.4% and bandwidth by 53.7%.

Keywords:

IPFS IoT LiDAR Time-Sensitive Networking autonomous driving systems blockchain data engineering distributed storage in-vehicle networks verifiable archival

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Published on: July 14, 2023

Area of Science:

Computer Science
Electrical Engineering
Robotics

Background:

Autonomous driving systems (ADSs) depend on LiDAR for perception, generating large data volumes.
High data rates strain in-vehicle storage, network bandwidth, and raise privacy concerns.

Purpose of the Study:

To propose an IoT data engineering framework for efficient LiDAR data processing, transmission, storage, and retrieval in ADSs.
To address data volume, bandwidth, and privacy challenges in autonomous vehicle sensor data management.

Main Methods:

Developed a framework combining error-bounded compression and blockchain-based storage over in-vehicle Time-Sensitive Networking (TSN).
Integrated AES-GCM encryption, blockchain smart contracts, and InterPlanetary File System (IPFS) for secure and tamper-evident data archival.
Utilized IEEE 802.1Qbv-based TSN scheduling for deterministic data delivery.

Main Results:

Achieved a 75.4% reduction in LiDAR data volume.
Resulted in a 53.7% reduction in total network bandwidth.
Demonstrated framework feasibility and effectiveness on the KITTI dataset.

Conclusions:

The proposed framework effectively manages high-volume LiDAR data in autonomous driving systems.
The integration of compression, blockchain, and TSN enhances data efficiency, security, and reliability.
The framework shows robustness and suitability for real-world autonomous vehicle applications.