Efficient congestion-aware routing in MANETs using quantum self-attention and optimized siamese network

Abstract

Mobile Ad Hoc Networks (MANETs) are dynamic, self-configuring wireless networks that frequently undergo topology changes, making them highly susceptible to link errors, congestion, and excessive energy consumption. Existing routing protocols struggle with real-time congestion detection and efficient path selection, leading to higher packet loss and increased network overhead. To address these challenges, this study proposes an adaptive Quantum Self-Attention-based Triple Pseudo Siamese Network (QSA-TPSN) with the Walrus Optimizer (WO) for congestion-aware and energy-efficient routing in MANETs. The QSA module enhances congestion detection by prioritizing stable, high-quality routes, reducing retransmissions by 25%. The TPSN framework learns congestion patterns and dynamically refines routing decisions to minimize delay. Meanwhile, the WO optimizer optimally selects paths based on real-time congestion and energy metrics, ensuring load balancing and efficient resource utilization. The QSA-TPSN-WO model is evaluated against CLEE, CL-QAERP, ESCL-PSO, and ANN-based routing approaches using packet delivery ratio (PDR), delay, throughput, energy consumption, and packet drop rate as performance metrics. Experimental results confirm that QSA-TPSN-WO achieves a 99.4% PDR, reduces packet loss by 28%, decreases energy consumption by 30%, and improves throughput by 15% compared to state-of-the-art methods. These findings demonstrate that QSA-TPSN-WO significantly enhances routing stability, congestion control, and energy efficiency, making it a robust solution for MANET environments.