## 4 Performance Evaluation  
In this section, we describe the settings of the simulation environment, as shown in Table 4, that we used for verifying and evaluating the performance of the proposed technique. We used OPNET 18.0 as the network simulator and implemented our proposed protocol (MC_TQR) and compared it with the routing protocols AOMDV, AOTDV, and TQR. The simulation was performed with 50 nodes in a partial mesh topology that was placed in a 1000 m \(\times\) 1000 m area. There were 49 sensor nodes and a fixed gateway node. The ratio of malicious nodes was set to 0-40%, and two types of attacks (gray hole and DoS attacks) were performed. In gray hole attacks, malicious nodes could randomly drop data packets, with a dropping ratio in the range of 0.4-0.8 [17]. In a DoS attack, malicious nodes could periodically send a certain amount of packets to the target node. Data information was fixed according to mission criticality by using the constant bitrate (CBR) traffic model by taking into account the type, size, and period of audio and video data transmissions in a tactical network environment [24; 25]. In addition, our experimental environment generated more data than the link bandwidth capacity of the path to mimic the tactical ad-hoc network conditions. The media access control (MAC) protocol included carrier sense multiple access with collision avoidance (CSMA/CA), and the physical layer (PHY) was set to 2 Mbps (operated in the soldier radio waveform) to imitate the resource-constrained ad-hoc communication in the military environment [26]. On the performance evaluation scale, the packet delivery ratio (PDR), end-to-end delay, and throughput were compared and analyzed. The PDR was calculated by considering the number of packets sent and received from the source node to the destination node, and the delay measured the end-to-end delay time from the time the packet was sent by the transmitting node to the time the packet was received by the destination node. The throughput was calculated by measuring the number of packets transmitted within a specified time. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Simulator & OPNET 18.0 \\ \hline Simulation time & 500 s \\ \hline Routing Protocols & AOMDV, AOTDV, TQR, MC-TQR \\ \hline Number of nodes & 50 \\ \hline Percentage of malicious nodes & 0–40\% \\ \hline \hline \end{tabular}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{VolP G.723.1 (24 bytes)} \\ \hline \multicolumn{2}{c}{Video surveillance H.264 (500 bytes)} \\ \cline{2-3} \multirow{2}{*}{Traffic type} & Lighting sensor, chat (100 bytes) \\ \cline{2-3}  & Fire alarm, health sensor, message (120 bytes) \\ \cline{2-3}  & CCTV camera, bulk data (2000 bytes) \\ \hline MAC & CSMA/CA \\ \hline PHY & 802.11b (2 Mbps) \\ \hline \(\mu\) & 0.5 \\ \hline \(w_{1}\) & 0.5 \\ \hline \(w_{2}\) & 0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Simulation environment settings. AOMDV, ad-hoc on-demand multipath distance vector routing; AOTDV, ad-hoc on-demand trusted-path distance vector routing. Figures 3-5 show the performance evaluation results when the malicious node percentage was 30%. Figure 3 shows the average rate of packet delivery over the elapsed time. AOMDV reported the lowest PDR results compared to the other schemes because the gray hole and DoS attacks could not be detected using its routing metric, which considered only the hop count, where the data were transmitted over the shortest hop. AOTDV and TQR selected a trusted path by using routing metrics that considered reliability, and therefore, they could transmit the data over a reliable path by detecting the malicious nodes. However, owing to the high ratio of malicious nodes (30%) and occurrence of bottlenecks because of data transmission through a single path, their average PDR result was less than 50%. The proposed MC_TQR scheme selected the trusted paths using routing metrics that considered both reliability and QoS.