## 4 Performance Evaluation
Load balancing was guaranteed because of the detection of malicious nodes and the distribution of data through trusted multiple paths. As a result, even though the malicious node ratio was 30%, our protocol showed a PDR performance of approximately 70%, which was the highest among all the schemes. Figure 4 shows the average end-to-end delay over the elapsed time. In the case of AOMDV, the route with the smallest number of hops was selected, and the data were transmitted using the hop count metric. As only the shortest hop path was considered, AOMDV did not detect the nodes performing the attacks and transmitted the data on the paths with such nodes, resulting in the highest end-to-end delay when the malicious node ratio was 30%. This was because the delay in the packet processing time at the intermediate nodes accumulated continuously owing to the DoS attack, resulting in an end-to-end delay of approximately 600 ms, which could result in a disastrous tactical network situation. Conversely, AOTDV and TQR periodically updated the most reliable path to transmit the data. TQR additionally used metrics to factor in the \(ETX\) and delay to update the trusted path faster than AOTDV, resulting in a lower end-to-end delay. However, because the data packets were transmitted over a single path, this resulted in an end-to-end delay of approximately 150 ms. MC_TQR showed the lowest end-to-end delay results compared with the other schemes because it detected the malicious nodes and then selected multipath to ensure load balancing. Figure 3: Average packet delivery ratio over elapsed time. Figure 5 shows the average throughput over the elapsed time. The throughput was determined by calculating the data transmission in terms of packet size, generation rate, and overhead (ACK, back-off, etc.) for a given unit time. In this experiment, various tactical data features were used, as shown in Table 4. MC_TQR, which used metrics that considered both reliability and QoS to search for a trusted path quickly and also guarantee load balancing, reported a throughput of approximately 2800 kb/s. In tactical ad-hoc networks, mission-critical data should be transmitted quickly and accurately, and the effectiveness of the proposed technique was confirmed by the throughput results, when compared with other schemes. TQR and AOTDV reported similar throughput results of approximately 1900 kb/s, with TQR showing a slightly superior throughput because of its use of metrics such as trustworthiness and QoS. AOMDV reported the lowest throughput (approximately 1600 kb/s) owing to its technique of transmitting data without updating the path, while remaining exposed to DoS and gray hole attacks. In tactical ad-hoc networks, such mission-critical data could not be processed effectively, which could cause major problems. Figure 4: Average end-to-end delay over elapsed time. Figure 5: Average throughput over elapsed time. Figure 6 presents the PDR results based on the percentage of existing malicious nodes. In this experiment, we compared and analyzed the PDR performance while increasing the proportion of malicious nodes in the network topology from 0 to 40%. The average PDR was measured by considering a base tactical network environment where there was a delay due to a bottleneck because of a higher data usage than the link bandwidth even in the absence of any malicious node. Overall, as the percentage of malicious nodes increased, the PDR decreased owing to the increased processing time required for performing and analyzing the trust evaluations. AOMDV displayed significantly lower PDR results as the percentage of malicious nodes increased. TQR and AOTDV showed similar reductions in the PDR results as the percentage of malicious nodes increased. The reason was that both schemes used similar trust-based metrics, so the data could be transmitted by selecting the same path according to the topology network.