## 4 Performance Evaluation
It is difficult to detect a node performing intelligent attacks using ATRP, but it transmits data distributed through Q-learning considering energy factors; hence, it shows a lower delay than TQR and AOTDV, although the malicious node ratio is 30%. However, The end-to-end latency was around 200 ms as the packet processing time increased at the intermediate node due to the intelligent attack, making it difficult to process mission-critical data effectively. AOTDV and TQR are also ineffective at detecting intelligent attacks; therefore, they are vulnerable to DoS attacks that generate various types of traffic. TQR uses a metric that considers both reliability and QoS factors, resulting in a lower E2E delay than AOTDV. However, it is difficult to guarantee the reliability of mission-critical data because of an E2E delay of about 300 ms. AOTDV shows the highest average E2E delay compared to other mechanisms because it uses only reliability metrics, not considering QoS factors while transmitting data over a single path. MC-TIRP showed the lowest E2E delay compared with the other mechanisms because it was learned considering reliability and QoS factors and distributed transmission according to the priority of mission-critical data. Figure 5 shows the throughput over elapsed time. Throughput was measured by estimating data transfers in terms of overhead, transmission rate, and packet size over a given time period. MC-TIRP quickly found a reliable path considering trust, QoS, and energy and showed a throughput of about 3200 kb/s. In MC-WSN, data must be transmitted accurately and quickly, and the efficiency of the method was confirmed through throughput. AOTDV and TQR showed similar results of around 2200 kb/s. ATRP is ineffective at detecting on-off attacks, but because it selects a path based on Q-learning, it shows higher throughput results than TQR and AOTDV. Figure 4: Average E2E delay over elapsed time. Figure 3: Average PDR over elapsed time. Figure 6 shows the normalized cumulative distribution function (CDF) of the end-to-end delay for mission-critical data A. CDF is calculated as the ratio of the number of received packets. Mission-critical data must satisfy performance requirements and guarantee QoS even in worst-case situations. The results show that MC-TIRP collects and processes large quantities of data faster than other techniques, despite the fact that 30% of the nodes are malicious. This is due to the learning of the trust and QoS elements that effectively excluded the malicious nodes so that the mission-critical data A was transmitted on the trusted paths. In the case of ATRP, it found the trusted path by learning the trust factor, but the CDF of end-to-end delay results was less than those of the proposed method because it rewarded only the energy factor and the priority of mission-critical data was not considered. However, mission-critical data were distributed and transmitted through learning considering the energy factor, resulting in higher CDF results than those of TQR and AOTDV. TQR and AOTDV show significantly lower CDFs according to the end-to-end delay compared to the proposed technique. This is because the data were transmitted using one path, and the DoS attack continuously caused a bottleneck. Figure 7 shows the results for the number of live nodes over time. To determine the number of live nodes, we set the initial energy constant values in a simulation environment without malicious nodes. To measure the residual energy, the rate was decreased every time a packet was transmitted. MC-TIRP and ATRP reflect efficient energy management because trust learning is performed by considering residual energy as a trust metric. Therefore, according to the elapsed time, all nodes were live to check the possible communication results. As TQR changed the path more frequently while considering reliability and QoS factors, more nodes survived than AOTDV. AOTDV shows the result of fewer nodes surviving because the path did not change significantly when using the reliability metric.