## 4 Performance Evaluation  
We introduce the simulation environment settings, as described in Table 2. The proposed protocol (MC-TIRP) was implemented using the OPNET and compared with the ATRP, IQR, and AOTDV. The simulation was deployed with 100 nodes in a partial mesh topology that was in a 1000 m \(\times\) 1000 m area. A fixed gateway node and 99 sensor nodes were used. We set the percentage of malicious nodes between 0 and 40% and performed on-off attacks (including DoS and gray hole attacks). An attacker can launch all the behaviors continuously, with 70% good behaviors and 30% bad behaviors [24; 25]. Malicious nodes performing grey hole attacks drop packets at a rate of 30%. A malicious node performing a DoS attack could send a large number of packets to the target node. DoS attacks generate various types of traffic, and the network situation changes dynamically. Figure 2: The overall process for MC-TIRP Scheme. We used a constant bit rate (CBR) data traffic model by considering the type, size, and sensor data transmissions in MC-WSNs [26; 27; 28]. In the experimental environment, a larger amount of data than the link bandwidth capacity was transmitted to mimic the MC-WSN condition. The PHY was set to 2 Mbps to imitate the resource-constrained communication in the tactical network [29; 30]. For performance evaluation, the end-to-end delay, throughput, and packet delivery ratio (PDR) were analyzed. Delay refers to the E2E delay from the time the sending node sent the packet to the time the destination node received the packet. Throughput was measured by taking into the number of packets transmitted within a given period. PDR was measured by taking into account the number of packets transmitted and received, and the energy was calculated by determining the initial energy and decreasing it according to the rate of packet delivery. Figures 3-7 shows the network performance evaluation when the proportion of malicious nodes is 30%. Figure 3 shows the average PDR over elapsed time. AOTDV and TQR use trust metrics to select the trust path; however, intelligent DoS attacks that generate various types of traffic are difficult to detect. Damage from on-off attacks was accumulated due to the percentage of malicious nodes (30%). We also set up a situation where a lot of sensor data exceeds the link bandwidth. Therefore, the overall performance of the packet forwarding rate is degraded. In the case of AOTDV and TQR, data transmission over a single path became the bottleneck, resulting in an average PDR of less than 50%. As it is difficult to detect on-off attacks using ATRP, damage accumulates, but the reward function considers only energy; thus, it showed a lower PDR result than the proposed technique. The proposed MC-TIRP scheme allocates trusted multipaths using Q-learning-based routing metrics that consider reliability, QoS, and energy. As a result, MC-TIRP showed a PDR of about 70%, showing the highest level among all mechanisms. Our scheme guarantees the load balancing effect by transmitting high-importance data through the max Q-value path and transmitting relatively low data distributed through other paths. \begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Values** \\ \hline Simulator & OPNET 18.0 \\ \hline Simulation time(s) & 500 \\ \hline Routing & MC-TIRP, ATRP, TQR, AOTDV \\ \hline Number of nodes & 100 \\ \hline Percentage of malicious nodes & 0–40\% \\ \hline Attack model & Gray hole attack, On-Off attack \\  & Denial of Service attack \\ \hline  & VoIP G. 723.1 (24 bytes) \\ \cline{2-3}  & Fire alarm, Chat (100 bytes) \\ \cline{2-3} Traffic Type (Avg. Packet Size) & Health, Temperature, Humidity Sensors (120 bytes) \\ \cline{2-3}  & Security, Smart Meter (200 bytes) \\ \cline{2-3}  & Bulk Data, CCTV Camera (2000 bytes) \\ \hline MAC & CSMA/CA \\ \hline PHY & 802.11b \\ \hline \(\alpha\) & 0.5 \\ \hline \(\mu\) & 0.9 \\ \hline \end{tabular}
\end{table} 
Table 2: Simulation environment settings. Figure 4 shows the E2E delay over elapsed time.