Artificial Intelligence in Wireless Networks: CNN Architectures and Their Potential to Optimize Enterprise Dynamic Routing

Authors

Keywords:

Convolutional neural networks, dynamic routing optimization, enterprise wireless networks, applied artificial intelligence, CNN architectures, correlational analysis

Abstract

Enterprise wireless networks face unprecedented challenges due to exponential data traffic growth, demanding sophisticated approaches for dynamic routing optimization that transcend traditional algorithm limitations, this research comparatively analyzed most relevant convolutional neural network architectures to identify those with greatest potential in enterprise wireless network data processing, examining artificial intelligence methodologies in dynamic routing optimization. A descriptive-correlational design integrated systematic documentary analysis of 22 specialized references with multivariate correlational techniques to characterize architectures according to technical and enterprise applicability criteria. Findings reveal three distinctive categories: high precision (ResNet, DenseNet) maximizing technical capabilities requiring extensive resources, balanced efficiency (EfficientNet) providing optimized trade-offs, and ultra-efficiency (MobileNet) prioritizing scalability with 85-90% performance using fraction of resources. Significant correlation between architectural complexity and classification precision was identified, confirming systematic trade-offs during technology selection. Optimization methodologies show trends toward hybrid approaches integrating CNNs with Graph Neural Networks and reinforcement learning. Results provide empirical foundations for informed adoption of artificial intelligence technologies, facilitating decisions that balance technical and operational considerations according to specific organizational contexts.

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Published

2025-10-01

How to Cite

Molina-Izurieta, R. E., Caisaguano-Caisaguano, L. A., Plazas-Durán, A. R., Chávez-Naranjo, M. de los Ángeles, & Decimavilla-Alarcón, D. C. (2025). Artificial Intelligence in Wireless Networks: CNN Architectures and Their Potential to Optimize Enterprise Dynamic Routing. Revista UGC, 3(S3), 202–213. Retrieved from https://universidadugc.edu.mx/ojs/index.php/rugc/article/view/242