## 5 Evaluation
### Simulation Framework  
The proposed UAV-assisted data collection framework is evaluated using a simulation model developed in AnyLogic (www.anylogic.com, accessed on 15 December 2022)--a multi-method simulation software that allows the integration of multiple modeling paradigms including discrete event, agent-based, and system dynamics modeling. The simulation model is designed to represent the behavior of the UAVs, fog nodes, and broker nodes, as well as their interactions in the data collection process. The simulation model includes several components including the fog sites, brokers, sensors and UAVs, the parameters for which are provided in Table 3. The UAVs are modeled as autonomous agents that can move around in a 2D space, collect data from different locations, and transmit data to the nearest fog node. The fog nodes are modeled as stationary nodes that receive UAV data and store them temporarily before transmitting them to the broker node. The broker node collects data from the fog nodes, processes them, and stores them in a database. The simulation model is parameterized with realistic values based on existing literature and real-world data. The energy consumption of the UAVs is modeled based on the type of UAV, speed and the distance traveled, while the transmission range of the UAVs and the fog nodes is modeled based on the signal strength and interference in the environment. The charging rate of the UAVs is also modeled based on the transaction costs and the amount of data transmitted. The simulation model is used to evaluate the performance of the proposed framework under different scenarios and conditions. The impact of the number of UAVs and fog nodes, the data collection rate, and the charging rate on the overall performance of the system is evaluated. The simulation model is also used to evaluate the robustness of the system under different types of attacks, such as denial of service attacks, spoofing attacks, and jamming attacks. The simulation results are analyzed and compared to existing literature to validate the effectiveness of the proposed framework. The simulation results show that the proposed framework can achieve higher data collection rates and better energy efficiency compared to existing methods. The simulation results also show that the proposed framework is robust against various types of attacks, demonstrating the effectiveness of the proposed intrusion detection system. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **Time in Sec** \\ \hline STDG [53] & 99.13 & 98.60 & 98.70 & 0.986 & 1848 \\ SU-IDS [44] & 99.13 & 99.65 & 98.60 & 0.991 & 15,243 \\ DPMH [69] & 98.95 & 95.82 & 95.81 & 0.958 & - \\ DC [70] & 98.55 & 98.22 & 99.59 & 0.983 & 34,986 \\ PCA-RF [71] & 99.60 & 99.60 & 99 & 0.996 & - \\
**Proposed** & **99.77** & **99.79** & **99.77** & **0.997** & **772** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison with other models. \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline Fog Sites & 3–6 \\ Fog Brokers & 3–6 \\ Sensors & Up to 600 \\ UAVs & 5–15 \\ \hline CPU & 3.60 GHz Inter Core i7-6700K \\ RAM & 8 GB \\ OS & Windows 10 Pro \\ \hline Simulator & AnyLogic \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation and System Specifications. In our proposed framework, we used the CICIDS2017 dataset to evaluate the performance of our UAV-assisted intrusion detection system. CICIDS2017 is a publicly available dataset for evaluating intrusion detection systems. It is a labeled dataset with various types of network traffic, including normal traffic and several types of attacks. The dataset contains a total of 80 features, including flow duration, protocol, source, and destination IP addresses, source and destination port numbers, and packet and byte counts.