Abstract

Edge Intelligence (EI) frameworks provide an effective approach for enabling real-time data processing and intelligent decision-making in distributed Internet of Things (IoT) environments. Conventional cloud-centric architectures often transmit large volumes of sensor data to centralized servers, resulting in increased latency, network congestion, bandwidth consumption, and privacy concerns. This framework integrates edge computing, artificial intelligence, machine learning, and distributed IoT technologies to process data closer to its source. Edge nodes perform real-time data filtering, feature extraction, inference, and decision-making, reducing dependency on remote cloud infrastructure. Lightweight machine learning models can be deployed on resource-constrained edge devices to support applications requiring rapid responses, such as smart healthcare, industrial monitoring, intelligent transportation, and smart cities.