## 4 System Design and Goals
### Modification to Message Intervals and New Message Variables
#### 4.4.5 DRI Function
Overall, these enhancements result in our protocol performing better in end-to-end delay compared to OLSR and OLSRv2 in static scenarios. With the implementation of mobility speeds, our proposed routing protocol demonstrates superior performance in end-to-end delay compared to OLSR, OLSRv2, and the work of [42] across various mobility speeds, as in Table 1.0 OLSR and OLSRv2 experience higher delays due to increased node movements and topology changes. In contrast, our protocol maintains lower end-to-end delay across all scenarios, showcasing its effectiveness in handling dynamic wireless environments and ensuring quicker data packet delivery compared to the conventional protocols. Table 1.1 Packet delivery ratio against number of nodes in static scenario. Table 1.2 Packet delivery ratio against number of nodes in mobility scenario. ## 8 Conclusions

In conclusion, our proposed routing protocol demonstrates significant improvements in energy efficiency, routing overhead, packet delivery ratio (PDR), and end-to-end delay compared to the standard OLSR and OLSRv2 protocols and other improved OLSR results given in the related literature, such as in the work of [42]. The protocol's efficiency is attributed to its ability to reduce overhearing, enhance link management, and optimize information base management through programmable listening and base chore periods. These enhancements make our protocol well-suited for dynamic wireless environments, ensuring reliable and efficient communication. Overall, our protocol offers a promising solution for improving the performance of D2D communication networks, paving the way for enhanced network scalability and reliability. For future work, several areas could be explored to further enhance the performance and capabilities of our proposed routing protocol. Firstly, we will investigate the protocol's performance in larger-scale networks with hundreds or thousands of nodes. This could provide valuable insights into its capacity, scalability, and robustness. Additionally, integrating machine learning techniques for dynamic MPR selection and routing decisions could further optimize the protocol's performance, especially in highly dynamic environments. Furthermore, conducting real-world experiments or field trials to validate the protocol's performance under realistic conditions would be beneficial. Table 1.3 End-to-end delay against number of nodes in various mobility speeds. Table 1.4 End-to-end delay against number of nodes in static scenario. **Author Contributions:** Conceptualization, S.M.B. ; methodology, S.M.B. ; software, S.M.B. ; validation, S.M.B. ; resources, S.M.B. ; data curation, S.M.B. ; writing original draft preparation, S.M.B. Formal analysis, O.Y.A. and M.S. ; Investigation, O.Y.A. and M.S. ; Review and Editing, O.Y.A. and M.S. ; Supervision, O.Y.A. and M.S. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Data Availability Statement:** No new data were created or analyzed in this study. **Conflicts of Interest:** The authors declare no conflict of interest. ## References

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