Demand-Driven Data Acquisition for Large Scale Fleets

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 s...

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Autores principales:	Philip Matesanz, Timo Graen, Andrea Fiege, Michael Nolting, Wolfgang Nejdl
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	sensor-data acquisition connected vehicles big data cloud computing floating car data data streaming Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/b876985d05c94fbbb11d3a6bb932886e
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id	oai:doaj.org-article:b876985d05c94fbbb11d3a6bb932886e
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spelling	oai:doaj.org-article:b876985d05c94fbbb11d3a6bb932886e2021-11-11T19:10:46ZDemand-Driven Data Acquisition for Large Scale Fleets10.3390/s212171901424-8220https://doaj.org/article/b876985d05c94fbbb11d3a6bb932886e2021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7190https://doaj.org/toc/1424-8220Automakers 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.Philip MatesanzTimo GraenAndrea FiegeMichael NoltingWolfgang NejdlMDPI AGarticlesensor-data acquisitionconnected vehiclesbig datacloud computingfloating car datadata streamingChemical technologyTP1-1185ENSensors, Vol 21, Iss 7190, p 7190 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	sensor-data acquisition connected vehicles big data cloud computing floating car data data streaming Chemical technology TP1-1185
spellingShingle	sensor-data acquisition connected vehicles big data cloud computing floating car data data streaming Chemical technology TP1-1185 Philip Matesanz Timo Graen Andrea Fiege Michael Nolting Wolfgang Nejdl Demand-Driven Data Acquisition for Large Scale Fleets
description	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.
format	article
author	Philip Matesanz Timo Graen Andrea Fiege Michael Nolting Wolfgang Nejdl
author_facet	Philip Matesanz Timo Graen Andrea Fiege Michael Nolting Wolfgang Nejdl
author_sort	Philip Matesanz
title	Demand-Driven Data Acquisition for Large Scale Fleets
title_short	Demand-Driven Data Acquisition for Large Scale Fleets
title_full	Demand-Driven Data Acquisition for Large Scale Fleets
title_fullStr	Demand-Driven Data Acquisition for Large Scale Fleets
title_full_unstemmed	Demand-Driven Data Acquisition for Large Scale Fleets
title_sort	demand-driven data acquisition for large scale fleets
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/b876985d05c94fbbb11d3a6bb932886e
work_keys_str_mv	AT philipmatesanz demanddrivendataacquisitionforlargescalefleets AT timograen demanddrivendataacquisitionforlargescalefleets AT andreafiege demanddrivendataacquisitionforlargescalefleets AT michaelnolting demanddrivendataacquisitionforlargescalefleets AT wolfgangnejdl demanddrivendataacquisitionforlargescalefleets
_version_	1718431606502326272

Demand-Driven Data Acquisition for Large Scale Fleets

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