Analysis and Halon-IOT Concept to Reduce Data Transmission Delays in IOT Networks
Abstract
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.