Optimization of V2X Communication for Autonomous Vehicles and SDVN-Based ITS Design in VANET Environments
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This paper presents an in-depth optimization strategy for Vehicle-to-Everything (V2X) communication tailored for autonomous vehicle networks and proposes a robust Intelligent Transportation System (ITS) architecture built upon Software-Defined Vehicular Networking (SDVN) within highly dynamic VANET environments. While traditional Vehicular Ad Hoc Networks (VANETs) offer decentralized communication among mobile nodes, they suffer from inherent limitations such as frequent topology changes, high latency, and unreliable data delivery. The proposed architecture integrates real-time mobility simulation (SUMO), discrete event network modeling (OMNeT++), vehicular communication (VEINS), wireless emulation (Mininet-WiFi), and programmable flow control via the Ryu SDN controller. Various urban traffic scenarios with differing vehicle densities (low, medium, high) were simulated, and the performance of the SDVN-based ITS was benchmarked against a conventional VANET model. The SDVN model demonstrated a reduction of up to 25% in latency, a 15% improvement in PDR, and up to 30% higher throughput, while incurring only a moderate increase in control overhead due to SDN signaling. These findings validate the feasibility and efficiency of SDVN-enhanced ITS frameworks for next-generation smart transportation infrastructures and offer valuable insights for large-scale deployment in future autonomous mobility ecosystems.
Abstract
This paper presents an in-depth optimization strategy for Vehicle-to-Everything (V2X) communication tailored for autonomous vehicle networks and proposes a robust Intelligent Transportation System (ITS) architecture built upon Software-Defined Vehicular Networking (SDVN) within highly dynamic VANET environments. While traditional Vehicular Ad Hoc Networks (VANETs) offer decentralized communication among mobile nodes, they suffer from inherent limitations such as frequent topology changes, high latency, and unreliable data delivery. The proposed architecture integrates real-time mobility simulation (SUMO), discrete event network modeling (OMNeT++), vehicular communication (VEINS), wireless emulation (Mininet-WiFi), and programmable flow control via the Ryu SDN controller. Various urban traffic scenarios with differing vehicle densities (low, medium, high) were simulated, and the performance of the SDVN-based ITS was benchmarked against a conventional VANET model. The SDVN model demonstrated a reduction of up to 25% in latency, a 15% improvement in PDR, and up to 30% higher throughput, while incurring only a moderate increase in control overhead due to SDN signaling. These findings validate the feasibility and efficiency of SDVN-enhanced ITS frameworks for next-generation smart transportation infrastructures and offer valuable insights for large-scale deployment in future autonomous mobility ecosystems.