An Intelligent Edge-Cloud Framework for Real-Time IoT Data Analytics Using Machine Learning
The rapid proliferation of Internet of Things (IoT) devices has resulted in an unprecedented increase in the volume of data they generate. Real-time processing and analysis of IoT data are essential for enabling timely decision-making and appropriate response actions. However, conventional cloudbased architectures are often unable to meet the stringent latency requirements of such applications. This paper presents a novel Edge-Cloud framework that facilitates real-time IoT data processing by leveraging AWS IoT Greengrass Core to deploy lightweight machine learning models at the edge while transmitting selected data to the AWS cloud for long-term storage and advanced analytics. The proposed framework implements Random Forest and XGBoost algorithms for intelligent data filtering, incorporating a confidence-based transmission policy that significantly reduces cloud-bound data traffic. The framework reduced the volume of data transmitted to the cloud by up to 91.45% and decreased inference latency by 79% compared with the conventional cloud-based approach. In addition, the framework is readily applicable to a wide range of domains, including urban infrastructure and healthcare. The effectiveness of the proposed Edge-Cloud framework was validated using the Numenta Anomaly Benchmark (NAB), where it achieved an accuracy of 96.8%, outperforming many comparable approaches reported in the literature. This intelligent edge-cloud continuum enables scalable, secure, and cost-effective real-time IoT applications by bridging local processing with cloud-based advanced analytics.