# An Efficient Void Aware Framework for Enabling Internet of Underwater Things
## 4 Results and Discussion
### Analysis of Results
The reason is that EVA works with successful packet delivery efficiently by using link quality to calculate route costs, reliable efficient shortest route mechanism, and void avoidance method. For example, the route cost computation chooses the sensor of high residual energy and good link quality, balancing energy consumption, establishing stable links, and guaranteeing the delivery ratio. The EVA-DF method will choose the fastest paths and prevent redundant forwarding, reducing the total amount of data packets transmitted while maintaining a suitable packet delivery ratio. Finally, void detection and avoidance routing algorithms are used to prevent void sensors, while selecting the best nodes improves the packet delivery ratio, which is especially important in sparse networks. Figure 8: Comparison of EVA with existing algorithms in terms of packet delivery ratio. Figure 7: Comparison of EVA with existing algorithms in terms of network lifetime. As shown in Figure 8, increasing the number of sensors to 50 increases the packet delivery ratio in EVA by around 8% and 5%, accordingly, when compared to VAPR and SPRE-PBR. Furthermore, increasing the number of sensors to 400 increases the packet delivery ratio for EVA to around 96%, implying that EVA achieves a better packet delivery ratio than SPRE-PBR and VAPR, which have packet delivery ratios of 4% and 2%, accordingly. Furthermore, the findings demonstrate that VAPR achieves a good packet delivery ratio comparable to EVA, although it consumes roughly 34% less energy than VAPR in the same situation. Figure 9 shows the efficiency of the proposed algorithms compared to VAPR and SPRE-PBR in terms of end-to-end delay. The use of the EVA-DF algorithm has a direct impact on reducing the delay. This is because it avoids the void nodes (i.e., once the data reach the void node, it causes some delay because of the use of the retransmission mechanism). Moreover, the use of sender-based techniques reduces the delay since the data forwarding phase did not employ a holding technique, meaning that the data packet will be broadcast directly to its one-hop neighbors. With increasing the number of sensors, the delay is increased as with increasing the number of nodes, the chance of retransmission techniques may be increased. For instance, with increasing the number of nodes to 300, the delay of EVA is decreased compared to VAPR and SPRE-PBR about 41% and 63%, respectively. It is remarkable as shown in Table 4 that the EVA framework outperforms SPRE-PBR 56.6% less in terms of energy consumption, 21.4% increase in packet delivery ratio, 72.7% increase in network lifetime, and 63.5% decrease in delay in the presence of a different number of nodes. On the other hand, the EVA outperforms VAPR in terms of Packet delivery ratio is 17.2% increase, 47.4% decrease in terms of delay and 36% decrease in energy consumption, and a 31.5% increase in terms of network lifetime as shown in Table 2. We do agree that the performance of the proposed EVA is very close to the VAPR in terms of network lifetime. However, it is noted that the suggested framework's total performance gains are considerable and obvious for wide area networks or with increasing the number of sensors in the network. As a result, we believe that in a realistic traffic environment, performance gains will be significant. We do realize that with 200 nodes, the end-to-end delay performance of SPRE-PBR did increase in a similar ratio as it was the case for 150 nodes and 250 nodes. However, we want to clarify that this is due to the average case scenario for drawing the results where we took 50 simulation runs for the similar network setting for each result data in our drawing. Figure 9: Comparison of EVA with existing algorithms in End-to-End Delay.