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

Abstract

The Internet of Things (IoT) has enabled the emergence of cyber-physical smart environments, which have grown significantly in recent years because of the widespread use of smart devices in smart cities, healthcare systems, industrial automation, and intelligent transportation networks. When processing large volumes of real-time data streams from IoT devices, however, conventional cloud-based processing systems are plagued by high latency, bandwidth usage, limited scalability, and security issues. To overcome those problems, this paper introduces an Edge AI supported IoT platform for real-time data processing in cyber physical smart environments. The suggested design incorporates lightweight AI models and edge computing to facilitate intelligent decisions at the edge, reduce latency for analysis, and optimize resource use. These elements collectively work to facilitate adaptive data processing and anomaly detection, forming the backbone of the architecture. These components collectively form the backbone of the architecture, providing support for adaptive data processing and anomaly detection. Experimental evaluation is carried out on live IoT data and the edge devices to evaluate latency, energy consumption, throughput and accuracy of AI inference. The outcomes show that the proposed framework can significantly reduce processing delay and energy usage compared to the traditional cloud-based systems, and also improves the system's scalability and intelligent response capability. The envisioned architecture is an efficient and scalable solution for the next-generation smart cyber-physical environments.