Abstract
In general, topology design is the most important framework in network communication. In this research, a star topology is adapted as an application. Normally, the star topologies are freely connected to the closed-loop systems and effectively created for all network applications. However, the failure of the central node leads to function error and system collapse. Once the network system gets collapsed then attaining the original states becomes difficult. To overcome this problem, a novel star topology is designed using the operad linear differential theory. This proposed mathematical star design efficiently reconstructs the network, when the collapse occurs. Moreover, the linear differential theory is mostly used to create a pertinent computational replica, and operad is perturbed with ideal algebras including associative and commutative properties. Furthermore, the proposed star topology design is applicable for Storage Area Network (SAN). Henceforth, the stability of the proposed method is determined using proper eigenvalues with specific theorems and conditions. The implementation of this research is done in the MATLAB platform. Thus, the proposed differential star topology is validated in the SAN application for data transmission. Besides, the proposed model is validated with other existing models using different key metrics to make the comparison assessment. Finally, the comparison results proved that the proposed linear differential theory is sufficient to be applied for SAN application with different network conditions.










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Notes
Linear polynomial is defined as any polynomial equation in the form of \(q(x) = mx + n\), where \(m\) and \(n\) represented as real numbers.
Taylor series is referred to as a sequence extension of a purpose regarding a point.
Diagnosable means, it estimates the identity of real numbers whether it is equal to zero or less than zero.
The critical point is the function of a solo real variable is \(f(x)\) is not differential in the domain of \(x_{0}\) or zero at its derivatives.
Here in the network topology, the hub is derived by the critical points of differential star node strategy.
References
Amiripalli, S. S., & Bobba, V. (2019). An optimal TGO topology method for a scalable and survivable network in IoT communication technology. Wireless Personal Communications, 107, 1019–1040. https://doi.org/10.1007/s11277-019-06315-z
Chen, B., Yao, N., Liu, W., Liu, J., Li, X., & Hao, X. (2019). Distributed topology control algorithm based on load balancing evaluation model in wireless sensor networks. Wireless Personal Communications, 109, 2607–2625. https://doi.org/10.1007/s11277-019-06698-z
Kuruba, P., & Dushyantha, N. D. (2020). Polygon based topology formation and information gathering in satellite based wireless sensor network. Wireless Personal Communications, 115, 203–237. https://doi.org/10.1007/s11277-020-07568-9
Singla, P., & Munjal, A. (2020). Topology control algorithms for wireless sensor networks: A review. Wireless Personal Communications, 113, 2363–2385. https://doi.org/10.1007/s11277-020-07331-0
Rajesh, D., & Jaya, T. (2020). A mathematical model for energy efficient secured ch clustering protocol for mobile wireless sensor network. Wireless Personal Communications, 112, 421–438. https://doi.org/10.1007/s11277-020-07036-4
Ghrist, R., & Peterson, V. (2007). The geometry and topology of reconfiguration. Advances in Applied Mathematics, 38(3), 302–323
Niroomand, P., & Shamsaki, A. (2020). Nilpotent lie algebras having the Schur multiplier of maximum dimension. QuaestionesMathematicae, 43(9), 1239–1246
Huang, Y., Li, W., Tian, F., & Meng, X. (2020). A fitness landscape ruggedness multiobjective differential evolution algorithm with a reinforcement learning strategy. Applied Soft Computing, 96, 106693
Bockmayr, M., Klauschen, F., Györffy, B., Denkert, C., & Budczies, J. (2013). New network topology approaches reveal differential correlation patterns in breast cancer. BMC Systems Biology, 7, 78. https://doi.org/10.1186/1752-0509-7-78
Jyothirmai, P., Raj, J. S., & Smys, S. (2017). Secured self-organizing network architecture in wireless personal networks. Wireless Personal Communications, 96(4), 5603–5620
Rivin, I. (2003). Combinatorial optimization in geometry. Advances in Applied Mathematics, 31(1), 242–271
Cui, L., Xu, C., & Li, G. (2018). A high accurate localization algorithm with DV-Hop and differential evolution for wireless sensor network. Applied Soft Computing, 68, 39–52
Chen, J., Chen, M., Wei, X., & Chen, B. (2019). Matrix differential decomposition-based anomaly detection and localization in NFV networks. IEEE Access, 7, 29320–29331
Elaziz, M. A., Li, L., Jayasena, K. P. N., & Xiong, S. (2020). Multiobjective big data optimization based on a hybrid salp swarm algorithm and differential evolution. Applied Mathematical Modelling, 80, 929–943. https://doi.org/10.1016/j.apm.2019.10.069
Yang, L. (2020). The rational spectral method combined with the laplace transform for solving the robin time-fractional equation. Advances in Mathematical Physics. https://doi.org/10.1155/2020/9865682
He, Y., Meng, Z., Xu, H., & Zou, Y. (2020). A dynamic model of evaluating differential automatic method for solving plane problems based on BP neural network algorithm. Physica A: Statistical Mechanics and its Applications, 556, 124845
Nguyen, D. D., Gao, K., Wang, M., & Guo-Wei, W. (2020). MathDL: Mathematical deep learning for D3R Grand Challenge 4. Journal of Computer-aided Molecular Design, 34, 131–147. https://doi.org/10.1007/s10822-019-00237-5
Lockett, A. J. (2020). Search and optimization in topological spaces. In: General-purpose optimization through information maximization. Natural Computing Series (pp. 91–116). Berlin: Springer.https://doi.org/10.1007/978-3-662-62007-6_5
Wang, J., Zhou, Y., & Xiao, F. (2020). Identification of multi-element geochemical anomalies using unsupervised machine learning algorithms: A case study from Ag–Pb–Zn deposits in north-western Zhejiang China. Applied Geochemistry, 120, 104679
Jacob, F., Pather, S. R., Huang, W. K., Zhang, F., Hao Wang, S. Z., Zhou, H., Cubitt, B., Fan, W., Chen, C. Z., Xu, M., Pradhan, M., Zhang, D. Y., Zheng, W., Bang, A. G., Song, H., de la Torre, J. C., & Ming, G. (2020). Human pluripotent stem cell-derived neural cells and brain organoids reveal SARS-CoV-2 neurotropism predominates in choroid plexus epithelium. Cell Stem Cell, 27(6), 937-950.e9
Bhatia, R., & Jain, T. (2020). A Schur-Horn theorem for symplectic eigenvalues. Linear Algebra and its Applications, 599, 133–139
Jiang, J., & Jiang, Y. (2020). Leader-following consensus of linear time-varying multi-agent systems under fixed and switching topologies. Automatica, 113, 108804
Sserwadda, A., & Rekik, I. (2021). Topology-guided cyclic brain connectivity generation using geometric deep learning. Journal of Neuroscience Methods, 353, 108988
Mavromatis, A., Papadopoulos, G.Z., Fafoutis, X., Elsts, A., Oikonomou, G., Tryfonas, T. (2016). Impact of guard time length on IEEE 802.15.4e TSCH energy consumption.In: 2016 13th annual IEEE international conference on sensing, communication, and networking (SECON) (pp 1–3). London. https://doi.org/https://doi.org/10.1109/SAHCN.2016.7732997
Ketover, D., & Zhou, X. (2018). Entropy of closed surfaces and min-max theory. Journal of Differential Geometry, 110(1), 31–71
Almajidi, A. M., & Pawar, V. P. (2019). A new system model for sensor node validation by using OPNET. Wireless Personal Communications, 108(4), 2389–2401
Hetmaniok, E., Pleszczyński, M., Różański, M., Wituła, R. (2018). Parametric-vector versions of the gerschgorintheorem and the brauertheorem. In: AIP Conference Proceedings, AIP Publishing (vol. 1978, no. 1).
Jazayeri, F., Shahidinejad, A., & Ghobaei-Arani, M. (2021). A latency-aware and energy-efficient computation offloading in mobile fog computing: a hidden Markov model-based approach. The Journal of Supercomputing, 77(5), 4887–4916
Karpics, I., Markovics, Z., Markovica, I. (2013). Topological modelling as a tool for analysis of functioning systems. In: Intelligent Systems: Models and Applications (vol. 3, pp. 237–253). Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-642-33959-2_13
Mason, K., Duggan, J., Howley, E. (2017). Neural network topology and weight optimization through neuro differential evolution. In: Proceedings of the genetic and evolutionary computation conference companion, ACM.
Dutta, M.K. (2020). Comparative study between star and mesh topology for the application in all-optical WDM network. In: Progress in Computing, Analytics and Networking (vol. 1119, pp. 255–263). Singapore: Springer. https://doi.org/10.1007/978-981-15-2414-1_26.
Vybíral, J. (2020). A variant of Schur’s product theorem and its applications. Advances in Mathematics, 368, 107140
Yang, C. F., & Bondarenko, N. P. (2020). Local solvability and stability of inverse problems for Sturm-Liouville operators with a discontinuity. Journal of Differential Equations, 268(10), 6173–6188
Jadlovská, I., & Džurina, J. (2020). Kneser-type oscillation criteria for second-order half-linear delay differential equations. Applied Mathematics and Computation, 380, 125289
Yusoff, N.H.M., Zakaria, N.A., Sikora, A. (2019). 6LoWPAN protocol in fixed environment: A performance assessment analysis.In: 2019 10th IEEE international conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), IEEE (vol. 2).
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Sundaram, K., Vellupillai, S. Designing a Novel Star Topology using Operad Linear Differential Theory. Wireless Pers Commun 120, 565–585 (2021). https://doi.org/10.1007/s11277-021-08478-0
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DOI: https://doi.org/10.1007/s11277-021-08478-0