  Handling the tremendous amount of network data, produced by the explosive
growth of mobile traffic volume, is becoming of main priority to achieve
desired performance targets efficiently. Opportunistic communication such as
FloatingContent (FC), can be used to offload part of the cellular traffic
volume to vehicular-to-vehicular communication (V2V), leaving the
infrastructure the task of coordinating the communication. Existing FC
dimensioning approaches have limitations, mainly due to unrealistic assumptions
and on a coarse partitioning of users, which results in over-dimensioning.
Shaping the opportunistic communication area is a crucial task to achieve
desired application performance efficiently. In this work, we propose a
solution for this open challenge. In particular, the broadcasting areas called
Anchor Zone (AZ), are selected via a deep learning approach to minimize
communication resources achieving desired message availability. No assumption
required to fit the classifier in both synthetic and real mobility. A numerical
study is made to validate the effectiveness and efficiency of the proposed
method. The predicted AZ configuration can achieve an accuracy of 89.7%within
98% of confidence level. By cause of the learning approach, the method performs
even better in real scenarios, saving up to 27% of resources compared to
previous work analytically modelled
