Abstract
This study presents a comparative performance analysis of cloud and fog computing architectures based on simulation modeling. Cloud computing offers numerous advantages, including scalability, on-demand resource allocation, simplified deployment of applications and services. However, this evolution critically depends on efficient data transmission - a domain where principles of radio physics, such as signal propagation, interference management, and channel optimization, play a pivotal role. Fog computing extends cloud capabilities to the network edge, enabling localized processing and reducing response time. Using the Eclipse IDE and Java, two network models were developed to analyze key metrics: energy consumption, transmission delay, and network traffic under varying node topologies. The simulation results show that fog computing significantly reduces latency and distributes energy consumption more efficiently than traditional cloud systems. Correlation matrix analysis reveals that while latency and energy usage in cloud systems increase sharply with the number of devices, fog architectures exhibit better scalability and resilience. These findings highlight fog computing as a viable solution for real-time and IoT applications requiring low-latency responses and efficient resource distribution.