Aug 2026· International journal of informatics and applied mathematics· Vol 9, pp. 31-49· 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.
Mobile Ad Hoc Networks (MANETs) face major routing problems because their network structure keeps changing, their energy levels are minimal, their nodes move between locations, and they lack any central network control systems. The traditional routing protocols Ad hoc On-Demand Distance Vector (AODV) and Ad hoc On-Demand Multipath Distance Vector (AOMDV) face major problems in dynamic environments because they use too much energy, their routes fail too often, and their network control demands become too high. 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. The proposed approach partitions the network into multiple zones and employs energy-aware leader node selection to manage routing operations efficiently. The system uses Q-learning to create an adaptive routing system that chooses the best routing paths according to current network conditions, including residual energy levels, node movement, traffic intensity, and link reliability. The proposed protocol performance assessment uses the NS-2.35 simulator to test different simulation conditions, which include various simulation durations, node mobility rates, network capacity, and simulation area size. The simulation results show that RML-ZEREM achieves better performance than traditional AODV and AOMDV protocols through its ability to increase throughput while decreasing energy usage, improving packet delivery ratio, and reducing routing overhead. The zone-based hierarchical structure enhances network stability and scalability for MANET systems that operate in dynamic environments. The RML-ZEREM protocol functions as an intelligent routing system that adjusts its operations to achieve energy efficiency through its framework, 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).
In flying ad hoc networks (FANETs), high node mobility, dynamic topology, and limited resources, such as energy and bandwidth, lead to unstable links and short-lived routes. In such an environment, although Q-learning-based routing methods are adaptable, they face serious challenges in practice due to large state space, high computational load, and slow convergence. To address these issues, this paper proposes a two-level Q-learning-based geographic routing protocol called TLQ-Geo for FANETs. This protocol integrates hierarchical decision-making with adaptive reinforcement learning. TLQ-Geo divides the routing process into two layers: the guided region selection (GRS) layer and the Q-learning-based routing (QRL) layer. The GRS layer determines a bounded search corridor between the source and the destination using a chain of intelligent decision points (IDPs), while the QRL layer performs distributed path optimization within this virtual corridor via Q-learning. By restricting the state space to the region guided by IDPs, TLQ-Geo significantly reduces convergence time and computational overhead. In addition, a dynamic inter-layer feedback mechanism periodically evaluates the performance of each IDP chain and adaptively reconfigures it under topology variations. Extensive simulations demonstrate that when the node density varies, TLQ-Geo achieves higher network lifespan (approximately 4.51%), improved packet delivery ratio (about 1.25%), lower routing overhead (around 3.38%), and better energy efficiency (about 20.79%), while the delay increases by about 13.84%, compared to three basic routing methods, namely QRCF, QRF, and QFAN. Also, when the node speed changes, TLQ-Geo yields better network lifespan (approximately 5.46%), higher packet delivery ratio (about 1.69%), lower overhead (around 2.80%), and better energy efficiency (about 6.42%), while the delay increases by about 9.09%.
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
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.
Ronild Hako, E. Spaho, A. Annuk· Network· 0 citations