Deep Learning-Based Traffic Prediction and Adaptive Slice Scaling for Massive IoT Communications
Keywords:
massive IoT, network slicing, traffic prediction, deep reinforcement learning, adaptive scaling, system architecture, 5G, sustainabilityAbstract
The proliferation of massive Internet of Things (IoT) deployments introduces unprecedented spatial and temporal heterogeneity in network traffic, challenging the static resource provisioning assumptions on which current mobile architectures are built. Network slicing offers a path toward isolated, service-tailored logical networks, yet existing slice management frameworks rely predominantly on reactive threshold-based scaling that cannot track bursty, non-stationary IoT traffic patterns. This paper presents a system-level investigation of deep learning-based traffic prediction coupled with adaptive slice scaling as a unified control paradigm for massive IoT communications. We argue that the integration of advanced spatiotemporal prediction models with policy-driven reinforcement learning scaling agents constitutes a foundational shift from static over-provisioning toward anticipatory, self-optimizing network infrastructures. Our analysis spans the end-to-end system architecture, discussing orchestration layering, data pipelines, and the interplay between centralized inference and edge-native model execution. We examine the structural trade-offs among prediction accuracy, scaling reactivity, slice isolation guarantees, inter-tenant fairness, and energy sustainability. Critical attention is given to robustness under adversarial perturbations, model drift, and the lifecycle management of deep learning components in carrier-grade environments. Governance frameworks, standardization efforts, and economic mechanisms for multi-domain slice coordination are explored, highlighting the need for transparent, auditable decision-making. The proposed integrative viewpoint contributes a forward-looking research agenda that connects algorithmic innovation with the operational realities of large-scale, socio-technical communication systems.
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