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Mobile Networks and Applications
Erschienen in: Mobile Networks and Applications 6/2013

01.12.2013

A Sequential Patterns Data Mining Approach Towards Vehicular Route Prediction in VANETs

verfasst von: Amar Farouk Merah, Samer Samarah, Azzedine Boukerche, Abdelhamid Mammeri

Erschienen in: Mobile Networks and Applications | Ausgabe 6/2013

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Abstract

Behavioral patterns prediction in the context of Vehicular Ad hoc Networks (VANETs) has been receiving increasing attention due to the enabling of on-demand, intelligent traffic analysis and real-time responses to traffic issues. One of these patterns, sequential patterns, is a type of behavioral pattern that describes the occurrence of events in a timely and ordered fashion. In the context of VANETs, these events are defined as an ordered list of road segments traversed by vehicles during their trips from a starting point to their final intended destination. In this paper, a new set of formal definitions depicting vehicular paths as sequential patterns is described. Also, five novel communication schemes have been designed and implemented under a simulated environment to collect vehicular paths; such schemes are classified under two categories: RSU (Road Side Unit)-based and Vehicle-based. After collection, extracted frequent paths are obtained through data mining, and the probability of these frequent paths is measured. In order to evaluate the effectiveness and efficiency of the proposed schemes, extensive experimental analysis has been realized.
Vorheriger Artikel A Cluster-Based Beaconing Approach in VANETs: Near Optimal Topology Via Proximity Information
Nächster Artikel Detecting Hot Road Mobility of Vehicular Ad Hoc Networks

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Metadaten
Titel
A Sequential Patterns Data Mining Approach Towards Vehicular Route Prediction in VANETs
verfasst von
Amar Farouk Merah
Samer Samarah
Azzedine Boukerche
Abdelhamid Mammeri
Publikationsdatum
01.12.2013
Verlag
Springer US
Erschienen in
Mobile Networks and Applications / Ausgabe 6/2013
Print ISSN: 1383-469X
Elektronische ISSN: 1572-8153
DOI
https://doi.org/10.1007/s11036-013-0459-6

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