Edge Nexus: Bridging AI and Data Engineering for Seamless Edge Computing

Abstract

Edge computing has emerged as a critical paradigm for real-time data processing, reducing latency, and minimizing bandwidth usage by bringing computation closer to data sources. However, efficiently managing and processing data at the edge remains challenging due to resource constraints and the complexity of AI-driven workflows. This paper proposes a simplified AI-optimized data engineering framework tailored for edge computing applications. The framework integrates lightweight machine learning models, optimized data pipelines, and edge-aware resource management to enhance efficiency and scalability. We evaluate the proposed framework using real-world IoT and edge computing scenarios, demonstrating significant improvements in processing speed, energy efficiency, and model accuracy compared to traditional cloud-centric approaches. Our findings suggest that AI-optimized data engineering can unlock new possibilities for intelligent edge applications in smart cities, healthcare, and industrial automation.