Latest revision as of 03:23, 2 February 2021

Abstract

hough connectivity services have been introduced already today in many of the most recent car models, the potential of vehicles serving as highly mobile sensor platform in the Internet of Things (IoT) has not been sufficiently exploited yet. The European AutoMat project has therefore defined an open Common Vehicle Information Model (CVIM) in combination with a cross-industry, cloud-based big data marketplace. Thereby, vehicle sensor data can be leveraged for the design of entirely new services even beyond traffic-related applications (such as localized weather forecasts). This paper focuses on the prediction of the achievable data rate making use of an analytical model based on empirical measurements. For an in-depth analysis, the CVIM has been integrated in a vehicle traffic simulator to produce CVIM-compliant data streams as a result of the individual behavior of each vehicle (speed, brake activity, steering activity, etc.). In a next step, a simulation of vehicle traffic in a realistically modeled, large-area street network has been used in combination with a cellular Long Term Evolution (LTE) network to determine the cumulated amount of data produced within each network cell. As a result, a new car-to-cloud communication traffic model has been derived, which quantifies the data rate of aggregated car-to-cloud data producible by vehicles depending on the current traffic situations (free flow and traffic jam). The results provide a reference for network planning and resource scheduling for car-to-cloud type services in the context of smart cities.

Original document

The different versions of the original document can be found in:

• http://arxiv.org/abs/1802.07475

• http://arxiv.org/pdf/1802.07475

• http://dx.doi.org/10.1109/vtcspring.2017.8108664

• http://xplorestaging.ieee.org/ielx7/8106913/8108170/08108664.pdf?arnumber=8108664,

http://dx.doi.org/10.1109/vtcspring.2017.8108664

• https://dblp.uni-trier.de/db/journals/corr/corr1802.html#abs-1802-07475,

https://arxiv.org/pdf/1802.07475.pdf,

https://arxiv.org/abs/1802.07475,

http://export.arxiv.org/pdf/1802.07475,

https://www.kn.e-technik.tu-dortmund.de/bibtexbrowser/bibtexbrowser.php?key=Pillmann2017car-to-cloud&bib=%2Fvar%2Fwww%2Fvhosts%2Fkn.e-technik.tu-dortmund.de%2Fhttpdocs%2F.cni-bibliography%2Fpublications%2Fcni-publications.bib,

http://export.arxiv.org/abs/1802.07475,

https://academic.microsoft.com/#/detail/2740687818