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Vehicular crowdsensing is anticipated to become a key catalyst fordata-driven optimization in the Intelligent Transportation System (ITS) domain.Yet, the expected growth in massive Machine-type Communication (mMTC) caused byvehicle-to-cloud transmissions will confront the cellular networkinfrastructure with great capacity-related challenges. A cognitive way forachieving relief without introducing additional physical infrastructure is theapplication of opportunistic data transfer for delay-tolerant applications.Hereby, the clients schedule their data transmissions in a channel-aware mannerin order to avoid retransmissions and interference with other cell users. Inthis paper, we introduce a novel approach for this type of resourceaware datatransfer which brings together supervised learning for network qualityprediction with reinforcement learningbased decision making. The performanceevaluation is carried out using data-driven network simulation and real worldexperiments in the public cellular networks of multiple Mobile NetworkOperators (MNOs) in different scenarios. The proposed transmission schemesignificantly outperforms state-of-the-art probabilistic approaches in mostscenarios and achieves data rate improvements of up to 181% in uplink and up to270% in downlink transmission direction in comparison to conventional periodicdata transfer.

A Reinforcement Learning Approach for Efficient Opportunistic Vehicle-to-Cloud Data Transfer