Details
Original language | English |
---|---|
Article number | 7190 |
Journal | Sensors |
Volume | 21 |
Issue number | 21 |
Early online date | 29 Oct 2021 |
Publication status | Published - 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
- Chemistry(all)
- Analytical Chemistry
- Computer Science(all)
- Information Systems
- Physics and Astronomy(all)
- Atomic and Molecular Physics, and Optics
- Biochemistry, Genetics and Molecular Biology(all)
- Biochemistry
- Physics and Astronomy(all)
- Instrumentation
- Engineering(all)
- Electrical and Electronic Engineering
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: Sensors, Vol. 21, No. 21, 7190, 01.11.2021.
Research output: Contribution to journal › Article › Research › peer review
}
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 -