  Opportunistic communications are expected to playa crucial role in enabling
context-aware vehicular services. A widely investigated opportunistic
communication paradigm for storing a piece of content probabilistically in a
geographica larea is Floating Content (FC). A key issue in the practical
deployment of FC is how to tune content replication and caching in a way which
achieves a target performance (in terms of the mean fraction of users
possessing the content in a given region of space) while minimizing the use of
bandwidth and host memory. Fully distributed, distance-based approaches prove
highly inefficient, and may not meet the performance target,while centralized,
model-based approaches do not perform well in realistic, inhomogeneous
settings. In this work, we present a data-driven centralized approach to
resource-efficient, QoS-aware dynamic management of FC.We propose a Deep
Learning strategy, which employs a Convolutional Neural Network (CNN) to
capture the relationships between patterns of users mobility, of content
diffusion and replication, and FC performance in terms of resource utilization
and of content availability within a given area. Numerical evaluations show the
effectiveness of our approach in deriving strategies which efficiently modulate
the FC operation in space and effectively adapt to mobility pattern changes
over time.
