| Title | Edge AI versus Cloud AI: A Comparative Study for Real-Time Intelligent Systems |
| Research Area | Edge AI |
| Abstract | The rapid growth of intelligent applications such as autonomous vehicles, smart surveillance, industrial automation, and healthcare monitoring has increased the demand for real-time data processing and low-latency decision-making. Traditional cloud-based Artificial Intelligence (Cloud AI) architectures rely on centralized data centers, which can introduce latency, bandwidth consumption, and privacy concerns. Edge AI has emerged as an alternative paradigm by bringing intelligence closer to data sources, enabling on-device or near-device inference. This paper presents a comprehensive comparative study of Edge AI and Cloud AI, focusing on architecture, performance, latency, scalability, security, and application suitability. Experimental analysis and case studies indicate that Edge AI significantly reduces latency and enhances privacy, while Cloud AI remains advantageous for large-scale training and complex analytics. The study concludes by identifying hybrid Edge–Cloud AI as a promising future direction for intelligent systems. |
| Keywords | Edge AI, Cloud AI, Artificial Intelligence, Real-Time Processing, Latency, Distributed Intelligence |
| Paper Status | Published |
| Volume | 1 |
| Issue | 6 |
| Published On | 18/11/2025 |
| Published File |
IJSRTD_4868.pdf
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