A Comparative Study of Computing Paradigms for Real-Time‎Image Processing: Cloud, Edge, And On-Device AI - Choi , Won-hyuk; Jung, Woo-Jin (International Journal of Basic and Applied Sciences, 2025)
With the growing demand for real-time image and video processing, selecting an efficient computing architecture has become increasingly ‎important. This study conducts a comparative analysis of three paradigms—cloud computing (CC), edge computing (EC), and on-device ‎edge computing (ODEC)—to determine the most suitable method for real-time applications. By evaluating their performance across varying ‎data volumes and network conditions, we identify the trade-offs in latency, scalability, and responsiveness. The findings reveal that cloud ‎computing suffers from increased latency due to transmission bottlenecks, while edge and on-device computing significantly reduce latency ‎through decentralized processing. Among the three, ODEC demonstrates the most consistent performance, particularly in environments ‎requiring large-scale data handling and minimal network dependence. These results suggest that on-device AI offers a promising direction ‎for future real-time systems by addressing the limitations of traditional architectures.
Keywords: Cloud Computing; Edge Computing; AI-Based On-Device Edge Computing; Real-Time Processing; Data Analysis