ITS-G5 is a communication system for vehicle-to-everything communication for road safety and traffic efficiency applications. On the lower layers, it is based on IEEE 802.11 standard and uses a simple ad hoc mode with a random medium access control scheme. Ad hoc networking is realized by a geographical routing scheme that provides single- and multi-hop communication over ITS-G5 links for periodic and event-driven broadcast messages. Specifically, contention-based forwarding (CBF) allows for efficient and reliable multi-hop packet transport by overhearing and timer-based transmission control. To cope with the channel congestion, decentralized congestion control (DCC) adjusts the message rate and ensures that the network load keeps below a predefined threshold of the bandwidth. To enforce a node’s message rate, a “ Gatekeeper” above the MAC and beneath CBF is added. Under high network load, this Gatekeeper introduces an additional queuing delay seen by CBF, which can cause its overhearing function to fail. This contribution studies multi-hop forwarding in the context of DCC with the Gatekeeper and LIMERIC as a rate adaptation algorithm. We propose a congestion-enabled forwarding scheme that, in comparison to existing approaches considering DCC and forwarding separately, restores the efficient operation of CBF, and improves the communication performance in terms of reliability and latency for mixed data traffic composed of single- and multi-hop packets with different priorities. The simulations show the performance improvements in a freeway scenario. An analysis assesses the boundary conditions for CBF and the Gatekeeper. Moreover, the analysis corroborates the simulation.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Evaluation of Congestion-Enabled Forwarding With Mixed Data Traffic in Vehicular Communications


    Beteiligte:


    Erscheinungsdatum :

    01.01.2020


    Format / Umfang :

    3874950 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Traffic Congestion Prediction Using Categorized Vehicular Speed Data

    Kumar, Manoj / Kumar, Kranti | Springer Verlag | 2022


    Machine Learning Solutions to Vehicular Traffic Congestion

    Chhatpar, Pavan / Doolani, Nimesh / Shahani, Sumeet et al. | IEEE | 2018


    5G-enabled vehicular communications and networking

    Cheng, Xiang / Zhang, Rongqing / Yang, Liuqing | TIBKAT | 2019


    COOPERATIVE TRAFFIC CONGESTION DETECTION FOR CONNECTED VEHICULAR PLATFORM

    GUNEY MEHMET ALI / GUO RUI / TIWARI PRASHANT | Europäisches Patentamt | 2023

    Freier Zugriff