Résumé
Opportunistic communications are expected to play
a crucial role in enabling context-aware vehicular services. A
widely investigated opportunistic communication paradigm for
storing a piece of content probabilistically in a geographical
area 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.