Adaptive Cloud-Edge Scheduler Using Lightweight AI Models for Real-Time IoT Streams

Abstract

The rapid growth of Internet of Things (IoT) devices has increased the volume of real-time data generated by sensors, smart devices, industrial controllers, and healthcare monitoring systems. Cloud-centric processing provides scalable computation, but it often introduces high communication latency, bandwidth overhead, and energy cost for delay-sensitive IoT streams. Edge and fog computing reduce response time by moving computa-tion closer to data sources, but efficient workload placement across edge, fog, and cloud resources remains a challenging scheduling problem. This paper proposes an adaptive cloud-edge scheduler using lightweight artificial intelligence models for real-time IoT stream placement. The framework dynamically selects edge, fog, or cloud execution based on stream priority, latency deadline, bandwidth availability, workload complexity, CPU uti-lization, memory utilization, queue length, energy availability, and historical scheduling success. Lightweight models such as Decision Tree, Logistic Regression, Random Forest, and Tiny Neural Network are considered to support fast inference under resource constraints. A latency-energy-aware cost function vali-dates model predictions and improves runtime decision quality. The framework also integrates explainable scheduling decisions and a human-in-the-loop override mechanism for operational governance. Experimental evaluation using a working prototype demonstrates adaptive distribution of IoT streams across edge, fog, and cloud layers, with 11 observed scheduling decisions, 55% success rate, 274.796 ms average latency, and 0.111 J average energy consumption. The results indicate that combining lightweight AI prediction with cost-aware validation improves scheduling flexibility, transparency, and practical applicability in real-time IoT systems.