Proposition d'approches de d\'eploiement des unit\'es de bord de route dans les r\'eseaux v\'ehiculaires

TL;DR

This paper proposes and evaluates four novel spatio-temporal RSU deployment methods for VANETs, aiming to reduce costs and maximize coverage in complex urban road networks through data mining and optimization techniques.

Contribution

Introduces a comprehensive RSU deployment framework with four innovative methods leveraging mobility pattern mining and optimization to improve coverage and reduce deployment costs.

Findings

SPaCov effectively covers frequent mobility patterns.

SPaCov+ enhances coverage by including rare mobility patterns.

HeSiC maximizes coverage within budget constraints.

Abstract

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.