Empirical evaluation of predictive channel-aware transmission for resource efficient car-to-cloud communication.

Nowadays vehicles are by default equipped with communication hardware. Thisenables new possibilities of connected services, like vehicles serving ashighly mobile sensor platforms in the Internet of Things (IoT) context. Hereby,cars need to upload and transfer their data via a mobile communication networkinto the cloud for further evaluation. As wireless resources are limited andshared by all users, data transfers need to be conducted efficiently. Withinthe scope of this work three car-to-cloud data transmission algorithmsChannel-Aware Transmission (CAT), predictive CAT (pCAT) and a periodic schemeare evaluated in an empirical setup. CAT leverages channel quality measurementsto start data uploads preferably when the channel quality is good. CAT'sextension pCAT uses past measurements in addition to estimate future channelconditions. For the empirical evaluation, a research vehicle was equipped witha measurement platform. On test drives along a reference route vehicle sensordata was collected and subsequently uploaded to a cloud server via a Long TermEvolution (LTE) network.