Edge AI-Enabled IoT Architecture for Real-Time Data Processing in Cyber-Physical Smart Systems

Abstract

The rapid expansion of Internet of Things (IoT) technologies and cyber-physical systems (CPS) has generated enormous volumes of real-time data from interconnected sensors, smart devices, industrial systems, autonomous vehicles, and intelligent infrastructures. Traditional cloud-centric processing architectures often suffer from high latency, bandwidth limitations, and scalability bottlenecks, making them unsuitable for latency-sensitive cyber-physical applications. Edge Artificial Intelligence (Edge AI) has therefore emerged as a transformative solution that enables intelligent data processing and decision-making directly at the network edge, closer to data sources. This research proposes an Edge AI-enabled IoT architecture for real-time data processing in cyber-physical smart systems. The proposed framework integrates edge computing, distributed artificial intelligence, IoT communication networks, and intelligent resource management mechanisms to support low-latency analytics, adaptive decision-making, and scalable real-time processing. The architecture utilizes edge nodes equipped with machine learning and deep learning models to perform local inference and distributed analytics while minimizing dependence on centralized cloud infrastructures. The framework incorporates data acquisition, edge preprocessing, intelligent analytics, real-time communication, and cloud-assisted coordination mechanisms within a unified distributed architecture. Experimental evaluation demonstrates that the proposed framework significantly improves processing latency, bandwidth efficiency, scalability, and real-time decision accuracy compared to conventional cloud-centric IoT systems. Furthermore, the architecture enhances reliability, privacy preservation, and operational efficiency in heterogeneous cyber-physical environments.