Hybrid Metaheuristic Adaptive QoS Routing Using Dual-Layer Deep Reinforced Swarm Learning in 5G-Enabled Materials with Secure Packet Clustering

Abstract

QoS routing in MANETs continues to present significant challenges in the era of emerging wireless technologies, particularly with the integration of 5G networks, silicon-based antenna systems, and advanced material-based communication devices. The dynamic nature of node mobility, energy limitations, and vulnerability to security attacks requires robust and intelligent routing frameworks for modern MANET environments. Conventional swarm intelligence-based routing models, although effective, often lack adaptivity to dynamic topologies and fail to ensure secure packet transmission in heterogeneous material-driven wireless environments.

To overcome these limitations, this paper proposes a novel Hybrid Metaheuristic Adaptive QoS Routing Framework that seamlessly integrates Ant Colony Optimization (ACO), Red Deer Algorithm (RDA), and Butterfly Optimization Algorithm (BOA), reinforced by a Deep Q-Learning (DQL) controller. The hybrid architecture intelligently adapts its optimization strategies based on real-time network parameters such as energy, link stability, and packet loss, leveraging DQL for parameter tuning. Furthermore, the proposed model incorporates a dual-layer secure packet scheduling system comprising modified TDMA scheduling and secure fuzzy-based packet clustering, ensuring encrypted data transmission even in complex 5G-enabled silicon antenna-based MANET nodes.

The proposed framework was extensively validated through simulations and exhibited superior performance across multiple QoS metrics. It achieved a 15.3% increase in throughput, 13.8% improvement in packet delivery ratio, 22.5% reduction in delay, and 18.1% energy savings compared to existing approaches like MACOPM, RDA-EQRP, and ISMBOQAR-PS. The study establishes this hybrid approach as a highly adaptive and secure solution for next-generation MANETs integrated with advanced materials, 5G technologies, and silicon-based antenna infrastructures.