A roadside unit deployment framework for enhancing transportation services in Maghrebian cities. (20th December 2019)

• Record Type: Journal Article
• Authors: Ben Chaabene, Seif; Yeferny, Taoufik; Ben Yahia, Sadok
• Other Names: Schifanella Claudio guestEditor.; Bracciali Andrea guestEditor.; Benkrid Soumia guestEditor.; Moussa Rim guestEditor.; Badir Hassan guestEditor.; Lo Moussa guestEditor.; Bellatreche Ladjel guestEditor.
• Is Part Of: Concurrency and computation. Volume 33:Number 1(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-12-20
• Subjects: coverage -- hypergraph -- minimal transversal -- RSU placement -- sequential patterns -- VANET; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5611
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Roadside units (RSUs) have a crucial role in maintaining vehicular ad hoc networks (VANETs) connectivity and coverage, especially, for applications gathering or disseminating nonsafety 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 (1) reduce the deployment cost by minimizing as far as possible the number of used RSUs and (2) to maximize the coverage ratio. In this paper, we introduce a spatiotemporal RSU deployment framework including three methods, namely, SPaCov, SPaCov+, and HeSPic. 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. HeSPiC is a budget‐constrained spatiotemporal coverage method that aims to maximize the coverage ratio subject to a budget constraint, which is defined in terms of RSU number. 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.