# Dynamic-RPL: Enhancing RPL-Based IoT Networks with Effective Support of Dynamic Topology Management
## 7 Results and Discussion  
The results presented in Table 3 indicate the QoS performance of the standard RPL and Dynamic-RPL for each experimental setup when the networks were stable. It can be noticed that both performed well considering all the collected QoS performance measurements. High PDR and throughput were achieved in addition to having the delay and ETX maintained at low levels. This is reasonable as the situation in the network is quiet without any stability or load issues. However, Dynamic-RPL exhibited slight increases in delay and ETX of less than 4% particularly in the multi-DODAG and relatively larger-scale setup of S2. Another increase of less than 5% in energy consumption can also be seen in the case of S2 as indicated in Figure 10a. These were caused with the higher network overhead imposed with Dynamic-RPL. Figure 11a shows that Dynamic-RPL increased the overhead rate by a little more than 20%. All these increases are attributed to the additional global communications maintained using Dynamic-RPL among the global-nodes, which constituted up to 30% of the topology. As a result, most of the wireless links became a bit more busy compared to the case of standard RPL. Despite such insignificant degradations, Dynamic-RPL was able to maintain almost similar overall QoS performance to standard RPL during the steady state of the network. However, standard RPL struggled when the situation in the network deteriorated. As presented in Table 4, it experienced reductions of up to 18% in PDR and throughput compared to its performance when the network was stable. It also ended up with increased delay and ETX by more than 48%. Figure 10b shows that the situation led to a high level  
Figure 11: Network overhead results: (**a**) during a steady state of the network, and (**b**) during adverse network conditions. Figure 10: Energy consumption results: (**a**) during a steady state of the network, and (**b**) during adverse network conditions. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Protocol** & **Setup** & **PDR** & **Throughput** & **Delay** & **ETX** \\ \hline \multirow{2}{*}{RPL} & S1 & 99.06 & 238.15 & 128.44 & 195.12 \\  & S2 & 95.43 & 352.89 & 130.26 & 243.51 \\ \multirow{2}{*}{Dynamic-RPL} & S1 & 98.78 & 235.01 & 128.87 & 197.96 \\ \multirow{2}{*}{Dynamic-RPL} & S2 & 94.96 & 346.07 & 133.86 & 252.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: QoS Performance Results During a Steady State of the Network. of energy consumption with increases of more than 67%. In addition, network overhead also noticeably increased as shown in Figure 11b. These results indicate that it became challenging for standard RPL to maintain high performance in the presence of stability and load issues across the network. Standard RPL lacks the means to rectify the situation and restore the network to its steady state. In the case of Dynamic-RPL, such an impact on the overall network performance was kept to a bare minimum. It successfully detected and reacted to the stability and load issues without serious damage to the network. As presented in Table 4, Dynamic-RPL was able to address the stability issues with very little sacrifice of QoS performance. Only 6% reductions in PDR and throughput as well as increases in delay and ETX were experienced at most. The energy consumption increased by 10% only as indicated with Figure 10b. Although Figure 11b shows that network overhead increased by less than 55%, this was very much lower than the case of standard RPL as discussed above. It is evident that Dynamic-RPL can function effectively under deteriorated network situations compared to standard RPL. The results show that it was able to maintain a higher QoS performance of approximately 20% than standard RPL. It also managed to lower the energy consumption and network overhead by more than 30% and 60%, respectively. The ability of Dynamic-RPL to adapt allowed the network to successfully retain its steady state. However, it is important to understand how responsive it was to the situations in both experimental setups.