  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.
