## 4 Performance Evaluation
It was found that approximately 20% of the nodes did not operate because there was no residual energy. If there are nodes that fail to operate in an MC-WSN, they will not be able to support mission operations, which can have devastating consequences. Figure 5: Average throughput over elapsed time. Figure 6: Normalized CDF of end-to-end delay for received mission-critical data A. Figure 8 shows the PDR versus the percentage of malicious nodes. The result of the percentage of malicious nodes can measure the performance of how quickly they react when attacked. In the MC-WSN environment, the transmission rate of mission-critical data is a very important factor, so accurate performance analysis is required. We measured the PDR by increasing the proportion of malicious nodes from 0 to 40%. The average PDR was calculated by taking into account MC-WSNs where there was a delay due to a bottleneck because of the large amount of mission-critical data usage compared to the link bandwidth. As the proportion of malicious nodes increases, they receive more attacks and increase processing time, which decreases PDR. AOTDV and TQR showed a decrease in the PDR as the percentage of malicious nodes increased. This is because both schemes suggest a method that assigns a single path. MC-TIRP has demonstrated its ability to transmit data more than other methods by using an algorithm that distributes and transmits data after learning a trust metric. ATRP has difficulty in detecting on-off attacks, but because it selects paths based on Q-learning, it shows higher PDR results than TQR and AOTDV. Figure 9 shows the E2E delay based on the proportion of malicious nodes. Considering MC-WSN, even without a malicious node, there can be a 40 ms delay because mission-critical data usage is set above the link bandwidth. As a result of the proposed MC-TIRP, the latency did not exceed 50 ms even when the proportion of malicious nodes increased to 40%. The proposed method was verified because the observed delay is lower than the 100 ms delay required in MC-WSN. Among the other schemes, ATRP did not meet the required delay as the proportion of malicious nodes increased. It is difficult to guarantee the QoS factor because ATRP considers only energy for the reward function. However, the mission-critical data were distributed and transmitted through learning considering energy factors, which resulted in a lower average E2E delay than that of AOTDV and TQR. In the case of TQR and AOTDV, which used a method that measured trustworthiness and QoS, a delay occurred because a single route was used for mission-critical data transmission. Figure 8: PDR plotted against the proportion of malicious nodes. Figure 7: Number of live nodes over elapsed time. Figure 10 shows the throughput based on the proportion of malicious nodes. Throughput can show similar standard deviation results depending on the situation, even with an increase in malicious nodes. The reason is that processing more large amounts of data at any given time can increase overall average throughput. AOTDV and TQR indicated a throughput of about 2100-2400 kb/s, which decreased when the proportion of malicious nodes increased. It is difficult to maintain throughput in MC-WSNs, which require ensured trustworthiness and transmission importance of data. ATRP uses Q-learning to adaptively select routes, resulting in a throughput of about 2500 kb/s. The proposed MC-TIRP showed a throughput of about 3300 kb/s even when the malicious node ratio increased to 40%, confirming that it can reliably process mission-critical data.