## 5 Experimental Results and Discussion
### Case Study: Drones  
To demonstrate the usefulness of our proposed system, we applied it in a usage scenario based on a monitoring system for intelligent drones. A drone, which is an unmanned aerial vehicle, is controlled remotely by a human operator or by an integrated autonomous system. Drones are often used in various fields to perform tasks that may be difficult or dangerous for humans. These tasks include aerial photography, 3D mapping, delivery, environmental monitoring, and more. Figure 11 illustrates the conceptual integration of drones with Fog nodes and Cloud data centers. \begin{table}
\begin{tabular}{c c} \hline \hline
**Matlab** & **iFogSim** \\ \hline Created in 1984 & Created in 2012 \\ \hline Used in various fields & Specific: specifically designed \\ of science & for engineering fields \\ \hline Uses its own Matlab & Used in conjunction with Java \\ \hline Language for scripting & \\ and Simulink for & \\ graphical modeling & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison between Matlab and iFogSim. At the first level, we have the Low-Altitude Platforms (LAPs). They serve as the primary data collection and sensing devices. Equipped with various sensors such as cameras, Lidar, thermal sensors, and environmental sensors, they capture environmental data and perform tasks such as aerial photography, surveillance, and environmental monitoring. These platforms typically operate at altitudes of up to a few hundred meters above ground level. Moving to the second level, we encounter High-Altitude Platforms (HAPs). HAPs operate at much higher altitudes compared to LAPs, typically in the stratosphere at altitudes ranging from 20 km to 50 km above ground level. These platforms can be stationary, such as tethered balloons or airships, or mobile, such as solar-powered drones or aircrafts. HAPs provide broader coverage and connectivity beyond the capabilities of LAPs, covering large geographical areas. In addition to their role in extending network coverage, HAPs can also function as Fog nodes, reducing latency and bandwidth requirements for transmitting data to centralized data centers. At the third level, we have the Proxy server and Cloud data center, which represent centralized elements responsible for further data refinement, storage, and management. The Proxy server acts as an intermediary between the Fog nodes and the Cloud data center, facilitating data transfer and providing additional services such as caching and security. These elements handle the processing and storage of data collected by LAPs and HAPs and facilitate more complex data analysis and decision-making processes. To simulate the mobility patterns of LAPs, the EPFL/mobility dataset [49], which includes timestamped GPS coordinates of taxi movements in the San Francisco Bay Area, was adapted for use in our Fog-based architecture. Although the dataset is in 2D, each taxi's GPS coordinate was mapped to a drone's position, with a synthetic altitude assigned based on predefined patterns typical of LAP operations (ranging from 80 to 150 m). This enabled the generation of service requests at the mapped locations, which were then processed by nearby Fog nodes or HAPs. This setup closely approximates real-world drone mobility in dynamic environments, enabling the evaluation of the proposed system's performance. The dataset was preprocessed by filtering out redundant and noisy data points and converting timestamp values into consistent simulation intervals. We implemented our proposed approach in the iFogSim2 simulator based on the conceptual architecture illustrated in Figure 11. In the sensor class of iFogSim, we define IoT components, including devices, sensors, actuators, and their respective properties and connections. The Fog device class specifies the hardware characteristics, architecture, and connectivity details of computational resources like Fog devices and the Cloud. Additionally, a Fog device operates as an FOCN, containing both a database and a local host for managing processing tasks.