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Know thy neighbor - a data-driven approach to neighborhood estimation in VANETs

: Roscher, Karsten; Nitsche, Thomas; Knorr, Rudi

Postprint urn:nbn:de:0011-n-4846172 (248 KByte PDF)
MD5 Fingerprint: 6bf40ecb7f978c2f580f26bf1437bccb
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Erstellt am: 28.2.2018

Institute of Electrical and Electronics Engineers -IEEE-:
IEEE 86th Vehicular Technology Conference, VTC Fall 2017. Proceedings : Toronto, Canada, 24–27 September 2017
Piscataway, NJ: IEEE, 2017
ISBN: 978-1-5090-5935-5
ISBN: 978-1-5090-5934-8
ISBN: 978-1-5090-5936-2
5 S.
Vehicular Technology Conference (VTC Fall) <86, 2017, Toronto>
European Commission EC
H2020; 636220; TIMON
Enhanced real time services for an optimized multimodal mobility relying on cooperative networks and open data
Konferenzbeitrag, Elektronische Publikation
Fraunhofer ESK ()
classification; feature selection; link estimation; machine learning; neighbor selection; neural network; simulation; vehicular ad hoc network; VANET; V2X; Vehicle-to-X; unicast; standard; reliability; estimation; Cams; adaptation model

Current advances in vehicular ad-hoc networks (VANETs) point out the importance of multi-hop message dissemination. For this type of communication, the selection of neighboring nodes with stable links is vital. In this work, we address the neighbor selection problem with a data-driven approach. To this aim, we apply machine learning techniques to a massive data-set of ETSI ITS message exchange samples, obtained from simulated traffic in the highly detailed Luxembourg SUMO Traffic (LuST) Scenario. As a result, we present classification methods that increase neighbor selection accuracy by up to 43% compared to the state of the art.