## 12 Simulation and Results
### Analysis of AE2E Delay, RREQ, RO, and PDR with Maximum Speed  
This section examines the routing schemes, including RDBF, EEPDBR, RSMB, 3 DUL, UL, and UDB [10; 11; 12; 13; 14; 15], comparing them against the proposed protocol under varying maximum node speeds from Figure 8a-d. Both deep and shallow water environments play a critical role in node density, which increases with depth and distance. Node mobility speed significantly impacts node density performance, so for this study, the mobility speed was fixed between 5 and 25 m/s for RREQ packets across all techniques.  
Figure 8a illustrates the Average End-to-End (AE2E) delay for CBR packets recorded at the destination. The AE2E delay follows an inverse trend to the PDR, showing that when node distribution is sparse, SRAL does not outperform RDBF or EEPDBR.  
The Packet Delivery Ratio (PDR) for RDBF, EEPDBR, and the proposed SRAL scheme is shown in Figure 8b. It is evident that EEPDBR and SRAL exhibit a bend in their curves when the number of nodes is around 98. At this point, RDBF achieves a PDR of 89.2%, but as node density increases, its PDR drops to 88%, showing a declining trend.  
The maximum speed analysis is conducted across different schemes, observing similar reactions in node mobility and the corresponding RREQ and RO ratios in Figure 8c,d. Each scheme exhibited varying behaviors; for example, RDBF and EEPDBR achieved control ratios of 21% and 27%, respectively, while UDB reached 19.6%, UL reached 15%, 3 DUL reached 17%, and RSMB reached 22%. The proposed scheme, however, maintained a significantly lower control ratio of only 15%. Despite variations in node mobility speed, all routing schemes successfully managed RREQ, RO, and enhanced PDR in a structured manner, as shown in Figure 8c. However, protocols EEPDBR and SRAL effectively limit this range and show a decreasing trend compared to RDBF, UDB, 3 DUL, UL, and RSMB. EEPDBR and SRAL exhibit reductions of 76.2% and 79.5%, respectively, compared to RDBF and especially UL. Under the same network conditions, these protocols show only a 2-4% increase. The normalized RO, as shown in Figure 8d, which includes RREQ, RREP, WFA, and RERR packets, demonstrates a significant reduction in RO for both EEPDBR and SRAL. As discussed in Figure 8c, the normalized number of RREQ for these protocols is reduced, leading to more than a 50% reduction in RO compared to RDBF and a 45% reduction compared to EEPDBR. Under the same network conditions, RDBF continues to increase with the rising maximum speed of up to 25 m/s, while EEPDBR and CGDBLR maintain close performance, likely due to their restricted RREQ range, contributing to the control of RO across RDBF, UDB, 3 DUL, UL, and RSMB.  
Figure 8c,d analyzes the normalized RREQ and RO at varying maximum speeds, demonstrating that all schemes exhibit a sufficient capability to manage and control localization errors to a certain extent. The core concept of SRAL is to optimize the routing path while limiting the number of RREQs. By reducing RREQ packet propagation, SRAL minimizes RREQ and RO. Under the same conditions, RDBF and EEPDBR exhibit significantly higher RREQ values, with RDBF at 12 and EEPDBR at 3.5, while SRAL maintains an average of around 2 across speed intervals of 5, 10, 15, 20, and 25 m/s. Conversely, RDBF shows a continuous increase in both normalized RREQ and RO values, which include packets of RREQs, RREPs, and RERs. The same trend is observed for PDR and AE2E delay analysis for SRAL (proposed scheme) and the baseline schemes in Figure 8c,d, respectively.