Car-to-Cloud Communication Traffic Analysis Based on the Common Vehicle Information Model.

Although connectivity services have been introduced already today in many ofthe most recent car models, the potential of vehicles serving as highly mobilesensor platform in the Internet of Things (IoT) has not been sufficientlyexploited yet. The European AutoMat project has therefore defined an openCommon Vehicle Information Model (CVIM) in combination with a cross-industry,cloud-based big data marketplace. Thereby, vehicle sensor data can be leveragedfor the design of entirely new services even beyond traffic-relatedapplications (such as localized weather forecasts). This paper focuses on theprediction of the achievable data rate making use of an analytical model basedon empirical measurements. For an in-depth analysis, the CVIM has beenintegrated in a vehicle traffic simulator to produce CVIM-complaint datastreams as a result of the individual behavior of each vehicle (speed, brakeactivity, steering activity, etc.). In a next step, a simulation of vehicletraffic in a realistically modeled, large-area street network has been used incombination with a cellular Long Term Evolution (LTE) network to determine thecumulated amount of data produced within each network cell. As a result, a newcar-to-cloud communication traffic model has been derived, which quantifies thedata rate of aggregated car-to-cloud data producible by vehicles depending onthe current traffic situations (free flow and traffic jam). The results providea reference for network planning and resource scheduling for car-to-cloud typeservices in the context of smart cities.