Abstract
Fog Computing represents a distributed computing infrastructure strategically positioned at the network's edge, acting as an intermediate layer between remote cloud services and the data-generating smart devices on the ground. Leveraging this concept, a flexible and efficient smart city design emerges, offering a diverse range of applications, including smart healthcare, car parking, power management, water management, and waste management. The implementation of Fog computing enables reduced data processing latency and equitable workload distribution across fog nodes. The smart city system comprises several layers, namely connection, real-time processing, neighborhood linking, main processing, and data server layers. The flexibility of this framework allows for the scaling up or down of layers depending on specific smart city applications. In a case study focused on Smart healthcare services, the iFogSim platform was utilized to evaluate the system's performance. Notably, the results demonstrated a significant reduction in network usage, data processing latency, and processing costs when compared to traditional cloud computing solutions. Consequently, this improvement in efficiency translated into an enhanced user experience, offering superior scalability and reliability to users utilizing smart city services, including healthcare facilities.
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