## 4 Experiments and Results
Correspondingly, the proposed DMCAP achieves a better link prediction rate at the optimal time complexity (milliseconds) as illustrated in Figure 5:0 As shown in Table 4, the time complexity of DMCAP for initiating reactive transmission falls between 328 and 489 milliseconds. Figure 5:1 Link quality prediction rate against malicious events. The measurements are huge for existing techniques compared with the proposed model. The time complexity denoted in Table 4 is calculated in milliseconds (time taken to complete the operations). Each channel has multiple links between source node and destination node. In this experiment, the number of channel links are varied from 1000 to 2000. As proposed, DMCAP uses suitable nonlinear regression models and multi-channel ensemble classifiers, channel assessment operations taken in each sensor node are evenly balanced. Consequently, the time taken to complete channel assessment procedures is minimized (328 ms to 489 ms) compared with other works. In this evaluation, SMC and OETN struggle to analyze the real-time channel data using conventional approaches. Hence these existing techniques are consuming more time to obtain classified attributes of multiple channels (1116 ms and 1208 ms, respectively). At the same time, the precision rate of these systems are not optimal. On the other side, the CRLR is consuming procedure operational time between 399 ms and 578 ms which is comparably closer to the proposed method. In this case, the proposed method utilizes the efficiency of multi-channel classifiers (ensemble) over reinforcement procedures to reduce the time complexity around the network. Figure 10 and Table 5 show the average routing delay (ms) and reactive link establishment rate (%) against channel uncertainties, respectively. As provided in Figure 10, the routing delay for each channel shall be reduced with proposed active channel assessment and reactive channel handling policies. Initiating the reactive data transmission in the network is impossible when the channel assessment system is not reactive and inefficient against channel interruptions. At this moment, the existing techniques produce 280 ms to 220 ms of routing delay for each channel. Significantly, the routing delay is directly proportional to the reactive link establishment rate during node or link failures on the channel. As indicated in Figure 10, the routing delay of the proposed model is minimal compared with other techniques for various iterations (10 iterative experiments). This can be related to Table 5 results of the proposed model. The results provided in Table 5 show the successful link establishment rate during failures. \begin{table} \begin{tabular}{c c c c c} \hline \hline
**Number of** & **DMCAP (ms)** & **SMC (ms)** & **CRLR (ms)** & **OETN (ms)** \\ \hline
1000 & 328 & 444 & 399 & 456 \\
1200 & 356 & 558 & 446 & 567 \\
1400 & 389 & 720 & 478 & 787 \\
1600 & 435 & 780 & 498 & 810 \\
1800 & 468 & 897 & 525 & 940 \\
2000 & 489 & 1116 & 578 & 1208 \\ \hline \hline \end{tabular}  \end{Against the performance of existing techniques, the proposed model shows limited routing delay as it is predicting the channel uncertainties actively. On the same way, Table 5 provides the ability of successful link establishment rate among uncertain channel problems and link disabilities. As the number of epochs increases, the proposed DMCAP model updates reactive channel management quality by obtaining the channel assessment attributes. This mechanism works better than other systems and DMCAP obtains 99.7% of reactive link establishment rate. At the same time, CRLR is the only existing technique producing average routing delay (210 ms as minimum) and better link establishment rate (90.8%) compared with other existing techniques. The reinforcement learning system of CRLR is the reason for optimal performance. In contrast, SMC and OETN are evenly generating more routing delay as they are not deeply understanding the channel behaviors through crucial learning and classification principles.