Dipl.-Inf.

Roscher, Karsten

Filters

2
2
2
Reset filters

Settings
Now showing 1 - 2 of 2
  • Publication
    Know thy neighbor - a data-driven approach to neighborhood estimation in VANETs
    ( 2017)
    Roscher, Karsten
    ;
    Nitsche, Thomas
    ;
    Knorr, Rudi
    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.
  • Publication
    Low-Delay Forwarding with Multiple Candidates for VANETs Using Multi-Criteria Decision Making
    ( 2016)
    Roscher, Karsten
    ;
    Jiru, Josef
    ;
    Knorr, Rudi
    Vehicular ad hoc networks (VANETs) are envisioned to support driver assistance and automated driving posing strict requirements on communication reliability and delay. To support these applications, we propose Low Delay Forwarding with Multiple Candidates (LDMC), a geographic routing approach combining the advantages of sender-based control and opportunistic forwarding. Candidates are ranked based on position, time since the last status update and neighborhood information using multi-criteria decision making. Priority-dependent timers reduce the contention among forwarders. Our evaluation for freeway and grid scenarios shows substantial improvement over existing protocols for real-time applications requiring 100 ms or less end-to-end delay.