Résumé:
The Internet of Things (IoT) is a rapidly growing field in recent years. It refers to a network of interconnected devices (sensors) whose number continues to increase. The data collected from these various sensors is processed and stored through Cloud Computing. However, due to the physical distance between the cloud and the sensors (especially those that require real-time data), this can sometimes cause malfunctions. To overcome this issue, Fog Computing has emerged as a solution that enables data processing closer to the physical IoT devices. A recommendation system deployed in Fog Computing would operate at the edge of the network (close to user devices), thereby reducing latency, improving the effectiveness of real-time recommendations, and reducing the load on the central cloud. The objective of this work is to propose a recommendation system and execute it on both Fog Computing and Cloud Computing platforms in order to compare the latency in both cases
