  as a novel client-based method for resource-efficient opportunistic
transmission of delay-tolerant vehicular sensor data. BS-CB applies a hybrid
approach which brings together all major machine learning disciplines -
supervised, unsupervised, and reinforcement learning - in order to autonomously
schedule vehicular sensor data transmissions with respect to the expected
resource efficiency. Within a comprehensive real world performance evaluation
in the public cellular networks of three Mobile Network Operators (MNOs), it is
found that 1) The average uplink data rate is improved by 125%-195% 2) The
apparently selfish goal of data rate optimization reduces the amount of
occupied cell resources by 84%-89% 3) The average transmission-related power
consumption can be reduced by 53%-75% 4) The price to pay is an additional
buffering delay due to the opportunistic medium access strategy.
