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Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
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Demand-Driven Data Acquisition for Large Scale Fleets.

Philip Matesanz1, Timo Graen1, Andrea Fiege1

  • 1Volkswagen Group, 30163 Hannover, Germany.

Sensors (Basel, Switzerland)
|November 13, 2021
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Summary

Automakers can now efficiently manage connected vehicle sensor data. A new system minimizes data acquisition per vehicle, meeting diverse stakeholder needs while complying with GDPR regulations.

Keywords:
big datacloud computingconnected vehiclesdata streamingfault-tolerant systemsfloating car datasensor-data acquisition

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Area of Science:

  • Automotive Engineering
  • Data Science
  • Cyber-Physical Systems

Background:

  • Automakers manage large connected vehicle fleets with increasing sensor data demands from diverse stakeholders.
  • Current systems are inefficient, with vehicles passively transmitting static sensor data, failing to meet varied requirements.
  • A need exists for systems that can handle diverse data demands from multiple tenants in a connected vehicle ecosystem.

Purpose of the Study:

  • To present a novel system for efficient, vehicle-specific minimization of sensor data acquisition in connected vehicles.
  • To address the challenge of fulfilling diverse data demands from multiple stakeholders in a multi-tenant vehicle environment.
  • To ensure compliance with General Data Protection Regulation (GDPR) for personal data collection.

Main Methods:

  • Developed a system integrating a native software component with an automaker's platform and a cloud-based data brokering service.
  • Implemented vehicle-specific data acquisition by mapping individual data demands to specific vehicles.
  • Ensured GDPR compliance for personal data collection with prior consent and enabled direct collection of non-personal data.
  • Provided sensor data via near real-time streaming or recorded trip files with consistency guarantees.

Main Results:

  • A performance evaluation with over 200,000 simulated vehicles demonstrated system effectiveness.
  • The system successfully increased server capacity on-demand during peak load.
  • Average processing time for streaming data was within 269 ms during peak load.
  • The architecture supports both native vehicle integration and retrofitted hardware solutions like OBD readers.

Conclusions:

  • The presented system efficiently manages diverse sensor data demands from connected vehicles.
  • The architecture offers a scalable and adaptable solution for automakers and large sensor network operators.
  • The system ensures data privacy and regulatory compliance while optimizing data acquisition.