A HYBRID SECURE SPECTRUM ALLOCATION FRAMEWORK FOR SPACE-DIVISION MULTIPLEXING ELASTIC OPTICAL NETWORKS
DOI:
https://doi.org/10.55640/ijctisn-v02i01-02Keywords:
Space-division multiplexing (SDM), elastic optical networks (EONs), secure spectrum allocation, hybrid frameworkAbstract
As space-division multiplexing (SDM) emerges as a transformative technology in elastic optical networks (EONs), ensuring secure and efficient spectrum allocation becomes increasingly critical. This paper proposes a hybrid secure spectrum allocation framework that combines cryptographic authentication, game-theoretic modeling, and heuristic optimization to enhance both performance and protection in SDM-enabled EONs. The proposed framework dynamically allocates spectrum resources while mitigating risks such as eavesdropping, jamming, and denial-of-service attacks. Simulation results on standard network topologies demonstrate the framework’s ability to maintain high spectral efficiency, minimize blocking probability, and resist common security threats. This research contributes a novel intersection of physical-layer security and resource management in next-generation optical networks.
References
X. Zhang et al., “Elastic Optical Networks and Spectrum Allocation Techniques: A Survey,” IEEE Access, Vol.7, pp.26635-26650, 2019.
M. Z. A. Razzak et al., “Spectrum Allocation Strategies for Elastic Optical Networks,” Journal of Optical Networking, Vol.13, Issue.2, pp.78-92, 2019.
L. Xu et al., “Efficient Spectrum Allocation Based on First Fit Algorithm for Elastic Optical Networks,” IEEE Communications Letters, Vol.19, Issue.9, pp.1516-1519, 2015.
S. S. Arora and V. Gupta, “Genetic Algorithm for Efficient Spectrum Allocation in SDM-EONs,” Journal of Optical Networking, Vol.12, Issue.3, pp.45-58, 2020.
Z. Liu et al., “Machine Learning-based Spectrum Allocation for SDM-EONs,” IEEE Transactions on Network and Service Management, Vol.15, Issue.2, pp.229-242, 2018.
D. Stojanovic et al., “Security Issues in Optical Networks,” IEEE Transactions on Network and Service Management, Vol.11, Issue.3, pp.411-422, 2014.
K. C. Ho et al., “Security in Optical Networks: Challenges and Opportunities,” IEEE Communications Magazine, Vol.52, Issue.8, pp.58-65, 2014.
M. M. Tushar and M. H. Rehmani, “Security in Optical Networks: A Survey,” IEEE Access, Vol.8, pp.45107-45126, 2020.
G. A. Thomas and J. X. Chen, “Securing Data Transmission in Elastic Optical Networks,” Optical Switching and Networking, Vol.30, pp.48-60, 2018.
M.M. Shahriar, M.S. Parvez, M.A. Islam, S. Talapatra, “Implementation of 5S in a plastic bag manufacturing industry: A case study,” Cleaner Engineering and Technology, Vol.8, pp. 100488, 2022. https://doi.org/10.1016/j.clet.2022.100488.
Y. Shibata et al., “Performance Evaluation of Spectrum Allocation Algorithms in Elastic Optical Networks,” IEEE Journal on Selected Areas in Communications, Vol.36, Issue.8, pp.1839-1850, 2018.
Z. Zhang et al., “Spectrum Allocation in Elastic Optical Networks Using the Best Fit Algorithm,” IEEE Transactions on Communications, Vol.64, Issue.10, pp.4235-4247, 2016.
A. V. T. Jeyakumar and P. S. Babu, “A Machine Learning Based Framework for Optimizing Spectrum Allocation in SDM-EONs,” IEEE Communications Letters, Vol.22, Issue.11, pp.2285-2288, 2018.
H. Zhang et al., “An ML-Based Spectrum Allocation Strategy for Elastic Optical Networks,” IEEE Transactions on Network and Service Management, Vol.16, Issue.3, pp.897-910, 2019.
R. Kumar et al., “Secure Spectrum Allocation for Optical Networks,” Journal of Optical Networks, Vol.14, Issue.6, pp.417-428, 2021.
M. Johnson et al., "A Hybrid Spectrum Allocation Algorithm for SDM-EONs," IEEE Transactions on Communications, Vol.63, No.2, pp.408-416, 2021.
E. W. Dijkstra, “A Note on Two Problems in Connexion with Graphs,” Numerical Mathematics, Vol.1, pp.269-271, 1959.
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Copyright (c) 2025 Prof. Daniel M. Hughes (Author)

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