Improved Efficiency Of Fog And Cloud Computing Paradigms

For effective energy management in smart grids, this research suggests a FOG computing architecture that bridges the gap between the edge and CLOUD. Facing data congestion in both traditional edge and CLOUD computing, the system utilizes a two-tiered approach. By preprocessing and filtering sensor data from buildings, edge nodes reduce latency and network traffic. The FOG layer in turn effectively filters data for potential CLOUD processing while doing real time analysis and decision-making depending on immediate needs. With latency reduction of up to $98.7 \%$, the simulation demonstrates significant benefits over CLOUD only approaches and enables real time energy adjustments that optimize resource utilization. Overall, scalability and efficient data handling overcome CLOUD limitations, while enhanced security minimizes reliance on a central CLOUD. This distributed brain for the grid paves the way for a smarter, more responsive energy landscape, powered by intelligence at the edge.