AI-DRIVEN REAL-TIME ANALYTICS IN EDGE COMPUTING ENVIRONMENTS

Abstract

The rapid growth of Internet of Things (IoT) devices and data-intensive applications has increased the demand for low-latency and scalable real-time analytics. Traditional cloudcentric architectures often suffer from high latency, bandwidth constraints, and limited responsiveness when processing time-sensitive data. This paper presents a scalable real-time analytics framework that integrates edge computing with artificial intelligence to enable intelligent data processing closer to the data source. The proposed approach leverages distributed edge nodes equipped with machine learning models to perform local inference and decision-making. By offloading computation from the cloud to the network edge, the framework reduces latency and improves system scalability. Experimental evaluation demonstrates enhanced response time, efficient resource utilization, and improved analytical accuracy compared to cloud-only solutions. The results highlight the effectiveness of edge AI in supporting real-time analytics for nextgeneration intelligent systems.