## 4 Results and Discussion
### Analysis of Results
This comparative study investigated state-of-the-art underwater literature to illustrate the corresponding analysis of performance gain. It can be noted that in terms of percentage, the average values obtained by TM2 RP were 20% and 25% for MRP and DBR, respectively. The justification behind this is the use of underwater features to distinguish network movements that have not been studied in any current research. The existing algorithms use the quality of service and the location of the underwater cluster heads to select the next forwarding nodes. However, the proposed system addresses the underwater network complexities, accomplished by better results regarding the packet delivery ratio. This result is illustrated in Figure 10 in a more technically comprehensible manner. Here, in close contrast, the average values obtained results by TM2 RP are provided to make it readable compared to the literature. This is also valuable for validating the outcomes shown in Table 2, and Figure 9 demonstrates that TM2 RP outperformed other techniques in terms of packet delivery ratio. The overlapping in Figures 7 and 9 was due to lower performance difference between the literature protocols under smaller network scenario with limited number of sensor nodes. In particular, the energy consumption performance of MRP and the proposed TM2 RP was quite close with a smaller underwater network of less than 100 underwater sensor nodes. This is due to the individual node's residual energy and depth information-based strategy of MRP, which works with small network environment. However, the energy consumption performance considerably degrades with a scaled network environment of 150 or more underwater sensor nodes. Similarly, the packet delivery ratio performance of DBR and MRP was quite close with smaller network scenario and the same with  
Figure 10: The statistical outcomes of the comparative protocols with regard to packet delivery ratio. 100 sensor nodes because of the similar depth-based forwarding approach without creating layers, which works precisely with a smaller number of nodes. Figure 11 below shows the comparison of network lifetime between DBR, MRP, and TM2 RP. In this figure, DBR and MPR have a lower network lifetime than TM2 RP. DBR utilizes depth information only, leading to selecting a lower depth, always ignoring the node's energy. As a result, the selected node will die soon, and the network lifetime is further reduced. In MRP, depth information with residual energy has been employed for selecting the next forwarding nodes, meaning then that the node with high residual energy has always been selected. Moreover, other nodes have not been selected, which directly impacts on the network's lifetime. On the other hand, TM2 RP calculates the route cost using link quality, residual energy, and depth information. In this case, the energy consumption has been balanced and further improves the network lifetime. In Table 4, the network lifetime TM2 RP is investigated compared to the literature in terms of percentage gain. The average values obtained by TM2 RP can be noted as 14% and 103% for MRP and DBR, respectively. The performance strengths can be explained by the lack of utilizing the underwater features in the existing algorithms, depending primarily on the quality of service and the position information of the sensor nodes. However, underwater features have been considered in the proposed protocol, resulting in significant efficiency gains as the network lifetime is longer. These obtained results in network lifetime are further represented in detail in Figure 12. Thus, network lifetime outcomes and observations are displayed in the deep association as it is comparatively analyzed. Consequently, it validates the outcomes displayed in Table 4 and Figure 11. Hence, in comparison with other state-of-the-art techniques, the proposed TM2 RP indicated a more extended network lifetime.