Jul 2026· Journal of Intelligent Computing and Networking· 0 citations· 29 references
TL;DR
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.
Abstract
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.
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 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
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
An in-depth review of energy-efficient routing protocols that have been developed for FANETs and highlights the main research challenges, such as high mobility, dynamic topology, routing overhead, scalability, and security, and discusses future research directions to design more intelligent and energy-aware routing protocols.
Ragvinder Kaur, Amit Sharma· Journal of Intelligent Decis...· 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
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).