## 5 Experimental Results and Discussion
### Performance Metrics, Results, and Discussion  
We simulated our proposed solution using iFogSim2. The simulated Fog environment comprises five Fog colonies, each containing 12 Fog nodes. Fog nodes exhibit heterogeneity in both their topological positions and the dynamic workloads they process. Their spatial distribution across distinct colonies, combined with the mobility of LAP drones, leads to varying drone-to-node distances and service request intensities. This results in non-uniform resource utilization and variable processing delays. Figure 11: Three-tier architecture for Fog-enabled drone systems, where arrows represent the interactions between the layers. Figure 12: Application model for IoT-enabled drones case study. Table 7 shows the configuration of the devices. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Device** & **CPU** & **RAM** & **Uplink** & **Downlink** \\
**Type** & **(MIPS)** & **(MB)** & **Bandwidth** & **Bandwidth** \\  & & & **(Mbps)** & **(Mbps)** \\ \hline Cloud data center & 44,800 & 40,000 & 100 & 10,000 \\ Proxy server & 2800 & 4000 & 10,000 & 10,000 \\ Fog device & 2800 & 4000 & 10,000 & 10,000 \\ End device & 1000 & 1000 & 10,000 & 270 \\ \hline \hline \end{tabular} The latency between devices is shown in Table 8.  
\end{table} 
Table 7: Configuration of devices. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Source** & **Destination** & **Latency** \\ \hline Sensor & Final device & 0.6 \\ Actuator & Final device & 0.1 \\ Final device & Fog device & 2.0 \\ Fog device & Proxy server & 4.0 \\ Proxy server & Cloud datacenter & 100.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The latency between devices. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Time Period (T)** & **1** & **2** & **3** & **4** & **5** \\ \hline Time (S) & 8 & 16 & 24 & 32 & 40 \\ \hline Number of services & 71 & 48 & 48 & 46 & 97 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Details for services in 5 time periods. are considered \"failed services\". Additionally, some services may require resources that are unavailable during a given time period, leading to their placement being deferred to the next period. These services are categorized as \"remaining services\". Figure 13 presents the results of various methods concerning metrics such as the number of performed services, failed services, and remaining services. As illustrated, HRS is superior to ODMA and ICA, methods with 278 services performed, 26 failed services, and six remaining services by 6.33% and 2.75%, respectively. The superiority of HRS can be explained by the fact that based on priorities, Fog nodes select requests with minimum time deadlines for execution. Figure 14 presents the Fog resource utilization metric for different methods, calculated and reported over five time periods (1 to 25 s). The results show that the HRS method achieves a higher utilization rate of Fog resources than the other methods. The results show that HRS increased the utilization rate of Fog resources compared to other methods. As illustrated, HRS improved this metric by 0.46% compared to ICA and 17.47% compared to ODMA. Each incoming service must wait until the following time period for placement. Additionally, placement planning and various delays contribute to this waiting time. Figure 15  
Figure 14: Comparison of the Fog resource utilization in different methods. Figure 13: Comparison of services performed, failed, and remaining in different methods after 5 time periods. presents the waiting time for different algorithms at the end of each time period. In this analysis, the total waiting time is calculated for each service, and then the average waiting time across all executed services is reported for each algorithm. As illustrated, HRS was able to reduce service waiting times over all time periods. Although some improvements such as a 6.33% increase in the number of successfully executed services may appear modest at first glance, their impact becomes more meaningful when considered in the broader context of system performance.