  Road Side Units (RSUs) have a crucial role in maintaining Vehicular Ad-hoc
Networks (VANETs) connectivity and coverage, especially, for applications
gathering or disseminating non-safety information. In big cities with complex
road network topology, a huge number of costly RSUs must be deployed to collect
data gathered by all moving vehicles. In this respect, several research works
focusing on RSUs deployment have been proposed. The thriving challenge would be
to (i) reduce the deployment cost by minimizing as far as possible the number
of used RSUs; and (ii) to maximize the coverage ratio. In this thesis, we
introduce a spatio-temporal RSU deployment framework including three methods
namely SPaCov/SPaCov+, HeSPic and MIP. SPaCov starts by mining frequent
mobility patterns of moving vehicles from their trajectories then it computes
the best RSU locations that cover the extracted patterns. Nonetheless, SPaCov+
extracts the frequent mobility patterns as well as the rare ones to enhance the
coverage ratio. HeSiC is a budget-constrained spatio-temporal coverage method
that aims to maximize the coverage ratio subject to a budget constraint, which
is defined in terms of RSUs number. MIP is a spatio-temporal coverage method
that aims to finding representative transactions from the sequential database
and computing coverage. Performed simulations highlight the efficiency and the
effectiveness of the proposed RSU deployment framework in terms of coverage
ratio, deployment cost, network latency and overhead.
