## 6 Results and Discussion
### Comparison with the ATI Approach  
In this section, we compare our proposed approach with the ATI approach. Figure 7 depicts the performance of both the AA and ATI approaches in the data plane for four concurrent applications.  
Concerning the delivery rate and delay, Figure 7a,b indicate that for the first three applications (first application, second application, and third application), the AA approach outperforms the ATI approach. For a network size of 196 nodes and in the case of the second application, the delivery rate and delay of the AA approach are 90.84% and 1.4 s compared with 62.2% and 3.27 s, respectively, for the ATI approach. This occurs because the AA approach allocates priority and adapts to the application's requirements dynamically. For the fourth application, the ATI approach outperformed the AA approach in terms of both delivery rate and delay since in the case of the AA approach, the fourth application has no priority and is accommodated using only one timeslot, as shown in Figure 6b.  
Figure 7c shows that the control overhead of the AA approach is higher than that of the ATI approach regardless of the number of nodes because the AA approach employs more control message types to meet the application's QoS requirements. For a network of 64 nodes, the control overhead value is 10,927.1 messages for the AA approach compared with 4036.7 messages for the ATI approach. The AA approach consumed more energy than the ATI approach, as seen in Figure 7d. In comparison with the ATI strategy, the AA approach increased energy usage by 14.3% and 16.27% for networks of 100 and 144 nodes, respectively. This is primarily due to the exchange of control messages. This mainly occurs due to the exchanged control messages. Since the ATI approach does not add any control messages, then the number of exchanged control messages after the network convergence is relatively low. This means that the nodes sleep for longer periods during the control  
\begin{table}
\begin{tabular}{c c c} \hline \hline Scenario & MCR & DTR \\ \hline Scen 1 & 180 s & DV \\ \hline Scen 2 & 60 s & DV \\ \hline Scen 3 & 300 s & DV \\ \hline Scen 4 & 480 s & DV \\ \hline Scen 5 & DV & 1/1/1/1 min \\ \hline Scen 6 & DV & 1/4/8/10 s \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameter’s values for the considered scenarios.  
timeslots. The nodes in the case of the AA approach, in turn, continue to exchange several control message types after the network convergence, and so should stay awake longer.  
Concerning the control plane, Figure 8 depicts the control delivery rate and control delay for the case of four applications running simultaneously. Figure 8a,b show that, in general, both approaches present similar values for the increasing number of nodes, and the ATI approach performs slightly better than the AA approach. For a network size of 144 nodes, the ATI approach increased the control delivery rate by 4.65% and decreased the control delay by 7.45% in comparison with the AA approach. This could be justified by the number of exchanged control messages after the network convergence for both approaches. This number is higher for the AA approach, which increases the probability of packet drop due to the buffer fullness and increases the time that the message waits to be processed.  
Table 5 summarizes the comparison between the AA and ATI approaches, highlighting the outperformed approach for each of the evaluation metrics.  
Figure 8: Comparison between AA and ATI approaches in the control plane (four applications’ case).  
Figure 7: Comparison between AA and ATI approaches in the data plane (four applications’ case).