Adaptive Routing and Security for Heterogeneous Networks ‎Using Quantum Key Distribution and Bat Optimized ‎Recurrent Neural Network

  • Authors

    • S. Nithya Assistant Professor, Department of Information Technology, PSNA College of Engineering and Technology, Kothandaraman nagar,‎ Dindigul, India
    • Krishna Prakash Arunachalam Departamento de Ciencias de la Construcción, Facultad de Ciencias de la Construcción Ordenamiento Territorial, Universidad Tecnológica Metropolitana, Santiago, Chile
    • S. Kanimozhi Assistant Professor, Department of Computer Science and Engineering, Chennai Institute of Technology, Chennai, India. ‎Research Scholar, Dr. MGR Educational and Research Institute, Chennai, India
    • Asha Rani Borah Professor, Department of Computer Science and Engineering, New Horizon College of Engineering, Bengaluru, India‎.
    https://doi.org/10.14419/rp152h11

    Received date: April 22, 2025

    Accepted date: June 22, 2025

    Published date: August 27, 2025

  • Heterogeneous Network; Quantum Key Distribution; Optimization Algorithm; Network Security; Quality of Service

  • Abstract

    In contemporary heterogeneous networks, the reliance on robust and secure communication protocols is increasingly critical due to the ‎rising sophistication of intruding techniques and diverse attack vectors. The dynamic nature of routing in these networks, coupled with ‎nodes of varying computational capabilities, poses a risk of routing attacks, which significantly compromise network security and ‎performance. To address these challenges, this paper introduces an advanced framework combining Post-Quantum Cryptography (PQC) ‎with Bat Optimization Algorithm (BOA) based Adaptive Quantum Routing RNN (AQR-RNN) to enhance security and routing efficiency. ‎Quantum Key Distribution (QKD) is employed to secure communications, thus providing a robust defense against threats. Simultaneously, ‎BOA- AQR-RNN is utilized to optimize routing efficiency, inspired by the echolocation capabilities of bats. This approach leverages AQR-‎RNN architectures to adaptively learn and predict routing paths, enhancing decision-making and optimization processes. The synergy ‎between QKD and BOA- AQR-RNN approach not only strengthens the security framework of heterogeneous network routing protocols ‎but also achieves superior Quality of Service (QoS) by dynamically optimizing routing strategies. The proposed methodology demonstrates ‎significant potential for advancing secured communication in Internet of Things (IoT) environments and other complex network ‎architectures‎.

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  • How to Cite

    Nithya, S. ., Arunachalam, K. P. ., Kanimozhi, S., & Borah, A. R. . (2025). Adaptive Routing and Security for Heterogeneous Networks ‎Using Quantum Key Distribution and Bat Optimized ‎Recurrent Neural Network. International Journal of Basic and Applied Sciences, 14(4), 708-720. https://doi.org/10.14419/rp152h11