This article analyzes published scientific works on reducing data transmission delays in IoT networks. Within the framework of the analysis, resource allocation approaches based on edge-cloud continuum, fog-edge architecture, partial offloading, serverless offloading, SLA-aware scheduling, and deep reinforcement learning were compared. Based on the selected works, the article proposes a new HALON-IoT (Hybrid Adaptive Latency-Optimized Network for IoT) conceptual model. This model combines mist/edge/fog/cloud layers to provide ultra-low latency for critical flows and elastic scalability for analytical tasks. The main innovation of the proposed model is the integration of packet-level prioritization, partial offloading, SLA-risk scoring, and SDN/DRL orchestration into a single control contour. The article is written in a conceptual format and requires experimental validation based on emulation or a real test environment at the next stage.
Analysis and Halon-IOT Concept to Reduce Data Transmission Delays in IOT Networks
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References
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