A Structured Comparative Analysis of Cloud, Fog, and IoT Computing with Performance Metric Classification

Abstract

Rapid increase in Internet of Things (IoT) devices has created a need for fast and efficient data processing. Cloud computing has been widely used because high computation and storage are provided, but delay is introduced due to centralized data handling. Fog computing has been proposed to reduce this delay by moving processing closer to data sources.
In this paper, a comparative study of Cloud, Fog, and Internet of Things (IoT) systems is presented. Differences in architecture, processing capability, latency, and resource management are examined in a clear manner. A layered structure is described to show how IoT devices, fog nodes, and cloud systems work together for data processing. Important performance metrics such as latency, energy consumption, resource utilization, and Quality of Service are also discussed for system evaluation.
It is observed that IoT devices mainly generate real-time data with limited resources, cloud systems handle large-scale processing with higher delay, and fog computing provides intermediate processing with reduced latency. Integration of these paradigms is necessary to support real-time and scalable applications such as smart cities, healthcare monitoring, and industrial systems.