Demand-driven data acquisition for large scale fleets

Research output: Contribution to journalArticleResearchpeer review

Authors

External Research Organisations

  • Volkswagen AG
View graph of relations

Details

Original languageEnglish
Article number7190
JournalSensors
Volume21
Issue number21
Early online date29 Oct 2021
Publication statusPublished - 1 Nov 2021

Abstract

Automakers manage vast fleets of connected vehicles and face an ever-increasing demand for their sensor readings. This demand originates from many stakeholders, each potentially requiring different sensors from different vehicles. Currently, this demand remains largely unfulfilled due to a lack of systems that can handle such diverse demands efficiently. Vehicles are usually passive participants in data acquisition, each continuously reading and transmitting the same static set of sensors. However, in a multi-tenant setup with diverse data demands, each vehicle potentially needs to provide different data instead. We present a system that performs such vehicle-specific minimization of data acquisition by mapping individual data demands to individual vehicles. We collect personal data only after prior consent and fulfill the requirements of the GDPR. Non-personal data can be collected by directly addressing individual vehicles. The system consists of a software component natively integrated with a major automaker’s vehicle platform and a cloud platform brokering access to acquired data. Sensor readings are either provided via near real-time streaming or as recorded trip files that provide specific consistency guarantees. A performance evaluation with over 200,000 simulated vehicles has shown that our system can increase server capacity on-demand and process streaming data within 269 ms on average during peak load. The resulting architecture can be used by other automakers or operators of large sensor networks. Native vehicle integration is not mandatory; the architecture can also be used with retrofitted hardware such as OBD readers.

Keywords

    Big data, Cloud computing, Connected vehicles, Data streaming, Fault-tolerant systems, Floating car data, Sensor-data acquisition

ASJC Scopus subject areas

Cite this

Demand-driven data acquisition for large scale fleets. / Matesanz, Philip; Graen, Timo; Fiege, Andrea et al.
In: Sensors, Vol. 21, No. 21, 7190, 01.11.2021.

Research output: Contribution to journalArticleResearchpeer review

Matesanz, P, Graen, T, Fiege, A, Nolting, M & Nejdl, W 2021, 'Demand-driven data acquisition for large scale fleets', Sensors, vol. 21, no. 21, 7190. https://doi.org/10.3390/s21217190
Matesanz, P., Graen, T., Fiege, A., Nolting, M., & Nejdl, W. (2021). Demand-driven data acquisition for large scale fleets. Sensors, 21(21), Article 7190. https://doi.org/10.3390/s21217190
Matesanz P, Graen T, Fiege A, Nolting M, Nejdl W. Demand-driven data acquisition for large scale fleets. Sensors. 2021 Nov 1;21(21):7190. Epub 2021 Oct 29. doi: 10.3390/s21217190
Matesanz, Philip ; Graen, Timo ; Fiege, Andrea et al. / Demand-driven data acquisition for large scale fleets. In: Sensors. 2021 ; Vol. 21, No. 21.
Download
@article{c609b73d8591460e9b0cb4ef55b555e5,
title = "Demand-driven data acquisition for large scale fleets",
abstract = "Automakers manage vast fleets of connected vehicles and face an ever-increasing demand for their sensor readings. This demand originates from many stakeholders, each potentially requiring different sensors from different vehicles. Currently, this demand remains largely unfulfilled due to a lack of systems that can handle such diverse demands efficiently. Vehicles are usually passive participants in data acquisition, each continuously reading and transmitting the same static set of sensors. However, in a multi-tenant setup with diverse data demands, each vehicle potentially needs to provide different data instead. We present a system that performs such vehicle-specific minimization of data acquisition by mapping individual data demands to individual vehicles. We collect personal data only after prior consent and fulfill the requirements of the GDPR. Non-personal data can be collected by directly addressing individual vehicles. The system consists of a software component natively integrated with a major automaker{\textquoteright}s vehicle platform and a cloud platform brokering access to acquired data. Sensor readings are either provided via near real-time streaming or as recorded trip files that provide specific consistency guarantees. A performance evaluation with over 200,000 simulated vehicles has shown that our system can increase server capacity on-demand and process streaming data within 269 ms on average during peak load. The resulting architecture can be used by other automakers or operators of large sensor networks. Native vehicle integration is not mandatory; the architecture can also be used with retrofitted hardware such as OBD readers.",
keywords = "Big data, Cloud computing, Connected vehicles, Data streaming, Fault-tolerant systems, Floating car data, Sensor-data acquisition",
author = "Philip Matesanz and Timo Graen and Andrea Fiege and Michael Nolting and Wolfgang Nejdl",
note = "Funding Information: Funding: This work was supported in part by the German Federal Ministry for Economic Affairs and Energy (Grant No. 01 MD 19007A).",
year = "2021",
month = nov,
day = "1",
doi = "10.3390/s21217190",
language = "English",
volume = "21",
journal = "Sensors",
issn = "1424-8220",
publisher = "Multidisciplinary Digital Publishing Institute",
number = "21",

}

Download

TY - JOUR

T1 - Demand-driven data acquisition for large scale fleets

AU - Matesanz, Philip

AU - Graen, Timo

AU - Fiege, Andrea

AU - Nolting, Michael

AU - Nejdl, Wolfgang

N1 - Funding Information: Funding: This work was supported in part by the German Federal Ministry for Economic Affairs and Energy (Grant No. 01 MD 19007A).

PY - 2021/11/1

Y1 - 2021/11/1

N2 - Automakers manage vast fleets of connected vehicles and face an ever-increasing demand for their sensor readings. This demand originates from many stakeholders, each potentially requiring different sensors from different vehicles. Currently, this demand remains largely unfulfilled due to a lack of systems that can handle such diverse demands efficiently. Vehicles are usually passive participants in data acquisition, each continuously reading and transmitting the same static set of sensors. However, in a multi-tenant setup with diverse data demands, each vehicle potentially needs to provide different data instead. We present a system that performs such vehicle-specific minimization of data acquisition by mapping individual data demands to individual vehicles. We collect personal data only after prior consent and fulfill the requirements of the GDPR. Non-personal data can be collected by directly addressing individual vehicles. The system consists of a software component natively integrated with a major automaker’s vehicle platform and a cloud platform brokering access to acquired data. Sensor readings are either provided via near real-time streaming or as recorded trip files that provide specific consistency guarantees. A performance evaluation with over 200,000 simulated vehicles has shown that our system can increase server capacity on-demand and process streaming data within 269 ms on average during peak load. The resulting architecture can be used by other automakers or operators of large sensor networks. Native vehicle integration is not mandatory; the architecture can also be used with retrofitted hardware such as OBD readers.

AB - Automakers manage vast fleets of connected vehicles and face an ever-increasing demand for their sensor readings. This demand originates from many stakeholders, each potentially requiring different sensors from different vehicles. Currently, this demand remains largely unfulfilled due to a lack of systems that can handle such diverse demands efficiently. Vehicles are usually passive participants in data acquisition, each continuously reading and transmitting the same static set of sensors. However, in a multi-tenant setup with diverse data demands, each vehicle potentially needs to provide different data instead. We present a system that performs such vehicle-specific minimization of data acquisition by mapping individual data demands to individual vehicles. We collect personal data only after prior consent and fulfill the requirements of the GDPR. Non-personal data can be collected by directly addressing individual vehicles. The system consists of a software component natively integrated with a major automaker’s vehicle platform and a cloud platform brokering access to acquired data. Sensor readings are either provided via near real-time streaming or as recorded trip files that provide specific consistency guarantees. A performance evaluation with over 200,000 simulated vehicles has shown that our system can increase server capacity on-demand and process streaming data within 269 ms on average during peak load. The resulting architecture can be used by other automakers or operators of large sensor networks. Native vehicle integration is not mandatory; the architecture can also be used with retrofitted hardware such as OBD readers.

KW - Big data

KW - Cloud computing

KW - Connected vehicles

KW - Data streaming

KW - Fault-tolerant systems

KW - Floating car data

KW - Sensor-data acquisition

UR - http://www.scopus.com/inward/record.url?scp=85118183585&partnerID=8YFLogxK

U2 - 10.3390/s21217190

DO - 10.3390/s21217190

M3 - Article

AN - SCOPUS:85118183585

VL - 21

JO - Sensors

JF - Sensors

SN - 1424-8220

IS - 21

M1 - 7190

ER -

By the same author(s)