Aug 2026· International journal of informatics and applied mathematics· 0 citations· 6 references
TL;DR
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.
Abstract
Vehicular Ad-Hoc Networks are an emerging paradigm within Intelligent Transportation Systems (ITS), enabling communication between vehicles (V2V) and/or vehicles and infrastructure (V2I). These networks aim to enhance road safety and improve the driving experience. However, due to the high mobility of vehicles and frequent changes in their geographical positions, ensuring reliable data delivery remains a significant challenge. Clustering has emerged as a promising technique to improve scalability, reduce overhead, and enhance routing stability in VANETs. This paper first introduces a new classification framework for clustering-based routing protocols according to their operational and decision parameters. It then proposes the Q-learning Weighted ClusterBased Routing Protocol (Q-WeCBR), which combines clustering with reinforcement learning. Q-WeCBR improves cluster head (CH) selection through a weighted selection function and a maintenance phase that ensures cluster stability. In addition, the integration of Q-learning enables the protocol to adapt intelligently to topology changes by selecting the most reliable routes.Simulation results obtained with OMNeT++ and SUMO 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.
The paper introduces RML-ZEREM to solve existing limitations, which functions as a Reinforcement Learning (RL) based Zone-Based Leader-Aware Energy-Efficient Routing Protocol for MANETs, which serves next-generation MANET applications.
Rani Sahu, Babita Rathore· Journal of Intelligent Compu...· 0 citations
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).
A two-level Q-learning-based geographic routing protocol called TLQ-Geo for FANETs, which significantly reduces convergence time and computational overhead and integrates hierarchical decision-making with adaptive reinforcement learning.
Mehdi Hosseinzadeh, Jawad Tanveer, Amir Masoud Rahmani et al.· Journal of King Saud Univers...· 0 citations
Simulation results indicate that HOA-MEPFL-CLCT-RP outperforms existing models in terms of Packet Delivery Ratio (PDR), energy efficiency, End-to-End Delay (E2D), and routing overhead.
Shaleena H, Sumangala K· International journal of com...· 0 citations
Vehicular Ad Hoc Networks (VANETs) are characterized by highly dynamic topologies, leading to frequent link breakages and challenging reliable routing. While clustering effectively mitigates topology instability, optimal Cluster Head (CH) selection and routing remain NP-hard problems. Despite various existing approaches, many current meta-heuristic routing protocols struggle to balance exploration and exploitation in highly dynamic VANET environments, often suffering from premature convergence and cluster instability under high mobility. To address these critical limitations, this paper proposes CRAHO, a novel hybrid meta-heuristic approach integrating the CSA and HHO for robust clustering-based routing in VANETs. Specifically, CSA is employed during the clustering phase to evaluate critical parameters—such as communication link quality and spatial distance—to form highly stable clusters. Subsequently, the routing phase leverages HHO based on distance metrics and node degrees to establish optimal, persistent inter-cluster paths. By formulating a comprehensive multi-objective fitness function, the CRAHO algorithm effectively coordinates exploration and exploitation. This approach guarantees QoS by minimizing routing overhead and end-to-end delay while maximizing the Packet Delivery Ratio (PDR). Simulation results demonstrate that the proposed CRAHO framework significantly outperforms benchmark routing protocols in maintaining network stability and optimizing data transmission in highly mobile vehicular environments. Specifically, compared to the baseline methods, CRAHO achieves improvements of 10.06% in network lifetime, 10.65% in throughput, 6.72% in PDR, and a 6.41% reduction in end-to-end delay.
Ataollah Sattari, Ali Ghaffari, Abbas Mirzaei· Discover Internet of Things· 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