Even though connectivity services have been introduced in many of the most recent car models, access to vehicle data is currently limited due to its proprietary nature. The European project AutoMat has therefore developed an open Marketplace providing a single point of access for brandindependent vehicle data. Thereby, vehicle sensor data can be leveraged for the design and implementation of entirely new services even beyond traffic-related applications (such as hyperlocal traffic forecasts). This paper presents the architecture for a Vehicle Big Data Marketplace as enabler of cross-sectorial and innovative vehicle data services. Therefore, the novel Common Vehicle Information Model (CVIM) is defined as an open and harmonized data model, allowing the aggregation of brandindependent and generic data sets. Within this work the realization of a prototype CVIM and Marketplace implementation is presented. The two use-cases of local weather prediction and road quality measurements are introduced to show the applicability of the AutoMat concept and prototype to nonautomotive applications.

While cars were only considered as means of personal transportation for a long time, they are currently transcending to mobile sensor nodes that gather highly upto-date information for crowdsensing-enabled big data services in a smart city context. Consequently, upcoming 5G communication networks will be confronted with massive increases in Machine-type Communication (MTC) and require resourceefficient transmission methods in order to optimize the overall system performance and provide interference-free coexistence with human data traffic that is using the same public cellular network. In this paper, we bring together mobility prediction and machine learning based channel quality estimation in order to improve the resource-efficiency of car-to-cloud data transfer by scheduling the transmission time of the sensor data with respect to the anticipated behavior of the communication context. In a comprehensive field evaluation campaign, we evaluate the proposed context-predictive approach in a public cellular network scenario where it is able to increase the average data rate by up to 194% while simultaneously reducing the mean uplink power consumption by up to 54%.

Upcoming 5G-based communication networks will be confronted with huge increases in the amount of transmitted sensor data related to massive deployments of static and mobile Internet of Things (IoT) systems. Cars acting as mobile sensors will become important data sources for cloud-based applications like predictive maintenance and dynamic traffic forecast. Due to the limitation of available communication resources, it is expected that the grows in Machine-Type Communication (MTC) will cause severe interference with Human-to-human (H2H) communication. Consequently, more efficient transmission methods are highly required. In this paper, we present a probabilistic scheme for efficient transmission of vehicular sensor data which leverages favorable channel conditions and avoids transmissions when they are expected to be highly resource-consuming. Multiple variants of the proposed scheme are evaluated in comprehensive realworld experiments. Through machine learning based combination of multiple context metrics, the proposed scheme is able to achieve up to 164% higher average data rate values for sensor applications with soft deadline requirements compared to regular periodic transmission.

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The provision of reliable and efficient communication is a key requirement for the deployment of autonomous cars as well as for future Intelligent Transportation Systems (ITSs) in smart cities. Novel communications technologies will have to face highly-complex and extremely dynamic network topologies in a Vehicle-to-Everything (V2X)-context and will require the consideration of mobility information into decision processes for routing, handover and resource allocation. Consequently, researches and developers require simulation tools that are capable of providing realistic representations for both components as well as means for leveraging the convergence of mobility and communication. In this paper, we present a lightweight framework for the simulation of vehicular mobility, which has a communications-oriented perspective by design and is intended to be used in combination with a network simulator. In contrast to existing approaches, it works without requiring Interprocess Communication (IPC) using an integrated approach and is therefore able to reduce the complexity of simulation setups significantly. Since mobility and communication share the same codebase, it is able to model scenarios with a high level of interdependency between those two components. In a proof-ofconcept study, we evaluate the proposed simulator in different example scenarios in an Long Term Evolution (LTE)-context using real-world map data.

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Although 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 CVIMcompliant 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 carto-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.

Nowadays vehicles are by default equipped with communication hardware. This enables new possibilities of connected services, like vehicles serving as highly mobile sensor platforms in the Internet of Things (IoT) context. Hereby, cars need to upload and transfer their data via a mobile communication network into the cloud for further evaluation. As wireless resources are limited and shared by all users, data transfers need to be conducted efficiently.Within the scope of this work three car-to-cloud data transmission algorithms Channel-Aware Transmission (CAT), predictive CAT (pCAT) and a periodic scheme are evaluated in an empirical setup. CAT leverages channel quality measurements to start data uploads preferably when the channel quality is good. CAT's extension pCAT uses past measurements in addition to estimate future channel conditions. For the empirical evaluation, a research vehicle was equipped with a measurement platform. On test drives along a reference route vehicle sensor data was collected and subsequently uploaded to a cloud server via a Long Term Evolution (LTE) network.