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Vehicular big data is anticipated to become the "new oil" of the automotiveindustry which fuels the development of novel crowdsensing-enabled services.However, the tremendous amount of transmitted vehicular sensor data representsa massive challenge for the cellular network. A promising method for achievingrelief which allows to utilize the existing network resources in a moreefficient way is the utilization of intelligence on the end-edge-cloud devices.Through machine learning-based identification and exploitation of highlyresource efficient data transmission opportunities, the client devices are ableto participate in overall network resource optimization process. In this work,we present a novel client-based opportunistic data transmission method fordelay-tolerant applications which is based on a hybrid machine learningapproach: Supervised learning is applied to forecast the currently achievabledata rate which serves as the metric for the reinforcement learning-based datatransfer scheduling process. In addition, unsupervised learning is applied touncover geospatially-dependent uncertainties within the prediction model. In acomprehensive real world evaluation in the public cellular networks of threeGerman Mobile Network Operators (MNOs), we show that the average data rate canbe improved by up to 223 % while simultaneously reducing the amount of occupiednetwork resources by up to 89 %. As a side-effect of preferring more robustnetwork conditions for the data transfer, the transmission-related powerconsumption is reduced by up to 73 %. The price to pay is an increased Age ofInformation (AoI) of the sensor data.

Client-Based Intelligence for Resource Efficient Vehicular Big Data Transfer in Future 6G Network