Jul 2026· 2026 6th International Conference on Electrical, Computer and Energy Technologies (ICECET)· pp. 1-7· 0 citations· 32 references
Abstract
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).
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
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.
Ronild Hako, E. Spaho, A. Annuk· Network· 0 citations
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
Simulation results obtained demonstrate that Q-WeCBR outperforms CBR, DSDV, and GPSR in terms of packet delivery ratio and throughput, confirming the effectiveness of clustering combined with learning-based routing for dynamic vehicular networks.
Ahlam Boussadia· International journal of inf...· 0 citations
The integration of communication networks into smart grids introduces stringent requirements for reliability, low latency,
scalability, and energy efficiency. Existing routing protocols — the Routing Protocol for Low-Power and Lossy Networks
(RPL) and Greedy Perimeter Stateless Routing (GPSR) exhibit complementary strengths and weaknesses across varying
network conditions. This paper proposes an Adaptive Hybrid Routing Framework (AHRF) that integrates RPL and GPSR
under a machine learning (ML)-driven decision engine. The system dynamically selects the most suitable protocol
based on real-time network features including link quality, node degree, residual energy, queue occupancy, and traffic
load. We present rigorous mathematical models of both protocols, formulate a composite utility function capturing
trade-offs among packet delivery ratio (PDR), end-to-end delay, throughput, and energy consumption, and integrate a
Random Forest classifier for adaptive protocol selection. The framework is validated through a custom discrete-event
packet-level simulator implementing log-distance path loss with shadowing over a 500×500 m wireless mesh with up
to 200 randomly deployed nodes across four operational scenarios. Results demonstrate that the proposed Hybrid-ML
framework achieves PDR improvements of up to 8.6% over standalone RPL in the density scenario and up to 110.7%
over GPSR under node failure conditions, while achieving 27.4% lower energy consumption per packet than GPSR in
dense deployments. The Random Forest classifier achieves 95.6% cross-validation accuracy. Feature importance analysis
reveals that average SNR (29.9%), SNR standard deviation (20.8%), and path diversity (15.9%) are the dominant
predictors of optimal protocol selection, providing interpretability to the ML component. The findings demonstrate that
ML-based hybridization of complementary routing protocols offers a resilient and energy-efficient routing solution for
next-generation smart grid neighborhood area networks.
Teslim Komolafe, Enoch Owoeye, Samuel A. Adegbola et al.· Energy Science, Engineering,...· 0 citations