An extensive evaluation of Software-Defined Networking integrated with two traffic engineering technologies, Multi-Protocol Label Switching (MPLS) and Segment Routing (SR), applied to the AODV and OLSR routing protocols demonstrates that SR with distance-based IS-IS metrics achieves the highest Packet Delivery Ratio (PDR) and lowest delay.
Abstract
Vehicular Ad Hoc Networks (VANETs) are essential components of Intelligent Transportation Systems (ITS), allowing communication exchanges between vehicles and road infrastructure elements. These networks face challenges from vehicular mobility, including frequent topology changes, link instability, and variable wireless channel quality. This paper presents an extensive evaluation of Software-Defined Networking (SDN) integrated with two traffic engineering technologies, Multi-Protocol Label Switching (MPLS) and Segment Routing (SR), applied to the AODV and OLSR routing protocols. Nine incremental configurations are evaluated for each protocol, ranging from the default protocol through MPLS-enhanced forwarding, SDN-based centralized optimization, combined SDN-MPLS and SDN-SR integration, to advanced configurations using distance-based IS-IS weighted topology metrics with both Fixed and Adaptive metric computation approaches. Two distinct SDN topology construction methods are compared: a Protocol-based approach that uses routing table entries with equal hop-count metrics, and a distance-based approach using IS-IS weighted metrics. The simulation uses a realistic urban topology with 50 vehicles and 5 RSUs, evaluated across several traffic patterns, representing different application types. Results demonstrate that SR with distance-based IS-IS metrics achieves the highest Packet Delivery Ratio (PDR) and lowest delay by leveraging RSU infrastructure as reliable forwarding relays. Moreover, the proposed SDN-SR framework reduces routing overhead and control-plane signaling, improving network resource utilization and thereby indicating its potential to enhance the energy efficiency of vehicular communication infrastructures.
To overcome the inherent compromises between proactive and reactive data transmission in Vehicular Ad-hoc Networks (VANETs), this research introduces a novel framework tailored for highly unstable vehicular topologies. The developed system, termed the Dynamic Hybrid Routing Protocol (DHRP), merges the Optimised Link State Routing (OLSR) and Ad-hoc On-Demand Distance Vector (AODV) algorithms. A core feature of this architecture is its cross-layer power management module, which dynamically recalibrates transmission strength and routing paths by analysing real-time vehicle clustering and speed metrics. Comprehensive evaluations conducted via NS-3 and SUMO indicate that the proposed DHRP significantly surpasses both contemporary benchmarks and standard baselines. Notably, the architecture achieves a Packet Delivery Ratio (PDR) exceeding 90%, limits communication latency to well below the critical 40 ms safety boundary, and slashes energy expenditure by up to 90%. By effectively solving the traditional routing dichotomy, DHRP offers a highly scalable and sustainable communication backbone vital for the reliable operation of future Intelligent Transportation Systems (ITS).
Wireless Mesh Networks (WMNs) are a key enabling technology for dynamic, infrastructure-limited IoT environments. The routing protocol is the central design choice in any WMN deployment because throughput, end-to-end delay, energy consumption and delivery reliability are directly affected by it. A systematic, simulation-based evaluation of two widely studied WMN routing protocols is presented: the reactive Ad hoc On-Demand Distance Vector (AODV, RFC 3561) protocol and the proactive Destination-Sequenced Distance-Vector (DSDV) protocol. Simulations were conducted in OMNeT++ 6.3 with the INET 4.5 framework across five network densities $(N \in\{10,20,30,40,50\}$ nodes) in a $1000 ~\mathrm{m} \times 1000 ~\mathrm{m}$ IEEE 802.11g area with a many-to-one UDP traffic pattern representative of IoT data collection. A density-dependent crossover was revealed at approximately $N=20$: lower delay was achieved by DSDV in sparse networks, whereas higher throughput, higher delivery reliability and lower energy consumption were achieved by AODV at higher densities. At $N=50, \approx 35 \%$ higher throughput, zero routing failures and $\approx 8 \%$ lower energy consumption are delivered by AODV. It is indicated by the MAC-layer contention behavior that DSDV's high-density degradation is mainly driven by IEEE 802.11 channel saturation rather than by routing-algorithm deficiencies. Deployment guidelines derived from these findings are provided.
Alá F. Khalifeh, Abdulla Ababneh, Iacovos I. Ioannou· IEEE Jordan Conference on Ap...· 0 citations
Vehicular ad hoc networks (VANETs) have emerged as a critical enabler of intelligent transportation systems, particularly when integrated with 5G infrastructure to achieve high-throughput, low-latency vehicle-to-everything (V2X) communication. Nevertheless, optimizing message routing in such environments remains a significant challenge, as the operational complexity and prohibitive cost of large-scale physical deployments severely limit empirical evaluation of alternative transmission strategies. This paper presents a stochastic Petri nets (SPNs) model for evaluating routing configurations in 5G-enabled vehicular ad hoc networks (5G-VANETs). The proposed model evaluates mean response time, drop probability, utilization, and throughput, enabling the identification of communication bottlenecks without requiring physical deployment. By abstracting the system's stochastic behavior through SPN formalism, the model supports both steady-state analysis and sensitivity evaluation under varying traffic workloads. Results demonstrate that Route 1, with direct RSU connection, achieves the lowest mean response time and highest throughput, while Route 3, which relays messages through a rear vehicle and an auxiliary RSU, yields the lowest drop probability. A sensitivity analysis based on Design of Experiments reveals that cloud capacity and cloud service time are the dominant factors affecting mean response time. The SPN model thus enables system architects to compare routing configurations, identify performance bottlenecks, and size infrastructure components without requiring physical deployment.
José Miquéias Araújo, L. Lopes, Luiz Nelson Lima et al.· Journal of Internet Services...· 0 citations
The proposed framework separates network control from forwarding, maintains a global view of vehicular network state, classifies V2X flows by service criticality, and dynamically selects routes and bandwidth allocations using delay, congestion, handover, and priority constraints.
Swadhin Singh, Swatantra Kumar, Mr. Rahul Kumar· International Journal of Adv...· 0 citations
These findings verify that MANET transmission is demonstrably enhanced and safer when trust, congestion, and QoS are all addressed concurrently at a particular transit level as opposed to individual-criterion techniques.
Sonia Singhal, A. Kush· JOURNAL OF MECHANICS OF CONT...· 0 citations
Unmanned aerial vehicles (UAVs) have been used in heterogeneous vehicular networks to enhance performance on extremely congested roads and areas with low coverage. Nevertheless, when aerial relays are added to the routing process, the routing occurred in a more complex environment. Routing protocols often favour UAV relay routes because UAV relay route can have better link quality and a small number of hops, but the routes developed from these routing protocols can lead to load imbalance between the aerial and terrestrial elements of the network and sometimes the UAV can be the bottleneck itself. This paper aims to utilize modern networking paradigms, i.e., Software-Defined Networking (SDN) and Fog Computing—to achieve routing operations in a heterogeneous, cluster-based Vehicular Ad Hoc Network (VANET). Fog nodes will take responsibility for offloading/performing the computational tasks involved in cluster formation, inter-segment routing between the aerial and terrestrial paths, and determining the optimal number of cluster heads. Fog nodes will use fuzzy logic and reinforcement learning to execute these tasks. The role of the SDN controller will be to manage traffic flow across fog cells using its global view of the multi-tiered network architecture which integrates heterogeneous vehicles with fog-layer connectivity. The proposed model was assessed visa a variety of routing protocols designed for UAV (Unmanned Aerial Vehicle)-assisted networks as well all routings used in traditional vehicular networks in several scenarios. The performance has proven to be far superior in a variety of aspects, including: the packet delivery ratio as a function of vehicle density and the aerial relay density; network utilization efficacy as a function of the harvesting node speed; and end-to-end delay as a function of ground node density. Finally, the results provide strong evidence on the success of the selective clustering method taken up in our model, as based on the dwell time of the cluster.
Saif Thamer Mohammed Museedi, Hardik Joshi· International journal of com...· 0 citations