  The exploitation of vehicles as mobile sensors acts as a catalyst for novel
crowdsensing-based applications such as intelligent traffic control and
distributed weather forecast. However, the massive increases in Machine-type
Communication (MTC) highly stress the capacities of the network infrastructure.
With the system-immanent limitation of resources in cellular networks and the
resource competition between human cell users and MTC, more resource-efficient
channel access methods are required in order to improve the coexistence of the
different communicating entities. In this paper, we present a machine
learning-enabled transmission scheme for client-side opportunistic data
transmission. By considering the measured channel state as well as the
predicted future channel behavior, delay-tolerant MTC is performed with respect
to the anticipated resource-efficiency. The proposed mechanism is evaluated in
comprehensive field evaluations in public Long Term Evolution (LTE) networks,
where it is able to increase the mean data rate by 194% while simultaneously
reducing the average power consumption by up to 54%.
