# Dynamic-RPL: Enhancing RPL-Based IoT Networks with Effective Support of Dynamic Topology Management
## 8 Conclusions  
The vulnerability to performance degradation and failure under adverse network conditions is still a serious challenge for RPL networks. No standard support is provided to address stability and load issues being experienced across the network. The evaluation results indicated the inability of standard RPL to adapt and maintain high overall performance during challenging network situations. However, Dynamic-RPL provides an effective solution to rectify such situations and restore the high performance of the network. It introduces dynamic topology management to the original RPL functionality without adding much to its design complexity. It is based on applying effective DODAG aggregation and partitioning in a dynamic and distributed manner for optimizing the network toward better performance and stability.  
The extensive evaluation of Dynamic-RPL exhibited its high robustness and efficiency considering different scenarios in varying-scale setups. In addition to revealing similar QoS performance to standard RPL during steady network conditions, Dynamic-RPL noticeably maintained higher performance in challenging situations. Compared to the standard RPL, it was able to achieve 20% higher QoS performance while lowering energy consumption and network overhead by more than 30% and 60%, respectively. The ability of Dynamic-RPL to successfully adapt allowed for restoring the network to its steady state in a responsive manner. Only a few seconds were required for successful dynamic topological adjustments and complete network convergence.  
It is evident that Dynamic-RPL provides effective functionality with enriched support toward practical network flexibility and dynamicity. It effectively guarantees competitive overall performance, better adaptation to network deteriorations, and fully dynamic functionality. This is important to enrich the efficiency and revive the potentiality of RPL for a broader range of IoT applications.  
The plan for future work is to implement and evaluate Dynamic-RPL in a physical testbed environment using real IoT devices with more realistic data communications. Another important consideration is investigating how the dynamic functionality of Dynamic-RPL can be utilized to address an effective security solution. The ability to dynamically adjust the topology in response to network changes can be further extended to defend against RPL security attacks. Moreover, incorporating additional modules to effectively support other networking aspects such as failure recovery and load balancing would be another research direction for the future.  
The researcher would like to thank the Deanship of Scientific Research, Qassim University for funding the publication of this project.  
**Data Availability Statement:** Data are available upon request.  
**Acknowledgments:** The researcher would like to thank the Deanship of Scientific Research, Qassim University for funding the publication of this project.  
**Conflicts of Interest:** The author declares no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Solution** & **PDR** & **OH Rate** & \
\begin{tabular}{c} **Fully Dynamic** \\ **Topology** \\ \end{tabular} &
\begin{tabular}{c} **Inter-routing** \\ **Support** \\ \end{tabular} \\ \hline
[17] (2020) & 85\% & 20 (per node) & \(\times\) & \(\times\) \\
[19] (2020) & 76\% & 75 & \(\times\) & \(\times\) \\
[20] (2019) & 92\% & 97 & \(\times\) & \(\times\) \\
[24] (2020) & 90\% & 82 & \(\times\) & \(\times\) \\ Dynamic-RPL & 91\% & 64 & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of Dynamic-RPL with Relevant Existing Solutions.