  Vehicular big data is anticipated to become the "new oil" of the automotive
industry which fuels the development of novel crowdsensing-enabled services.
However, the tremendous amount of transmitted vehicular sensor data represents
a massive challenge for the cellular network. A promising method for achieving
relief which allows to utilize the existing network resources in a more
efficient way is the utilization of intelligence on the end-edge-cloud devices.
Through machine learning-based identification and exploitation of highly
resource efficient data transmission opportunities, the client devices are able
to participate in overall network resource optimization process. In this work,
we present a novel client-based opportunistic data transmission method for
delay-tolerant applications which is based on a hybrid machine learning
approach: Supervised learning is applied to forecast the currently achievable
data rate which serves as the metric for the reinforcement learning-based data
transfer scheduling process. In addition, unsupervised learning is applied to
uncover geospatially-dependent uncertainties within the prediction model. In a
comprehensive real world evaluation in the public cellular networks of three
German Mobile Network Operators (MNOs), we show that the average data rate can
be improved by up to 223 % while simultaneously reducing the amount of occupied
network resources by up to 89 %. As a side-effect of preferring more robust
network conditions for the data transfer, the transmission-related power
consumption is reduced by up to 73 %. The price to pay is an increased Age of
Information (AoI) of the sensor data.
