# An Efficient Void Aware Framework for Enabling Internet of Underwater Things
## 4 Results and Discussion
### Analysis of Results  
This section discusses the result of the proposed algorithm comparatively with well-known algorithms called VAPR [23] and SPRE-PBR [17]. Figure 6 below shows the impact of increasing the number of sensors in terms of energy consumption. As illustrated in the figure below, the energy consumption is increased with increasing the number of sensors. The energy consumption of EVA is lower than VAPR and SPRE-PBR for some reasons: the employment of route cost during the forwarding phase helps in selecting the next forwarding nodes with lower energy and link quality among neighbors. Moreover, the use of the introduced void detection algorithm can efficiently suppress the void nodes from forwarding the data packets. In comparison with VAPR, the void aware method consumes high energy as the assignment of the directions in VAPR consumes more energy for beaconing messages that supplied by the sink. Furthermore, SPRE-PBR consumes high energy as it did not provide a solution to avoid the void nodes that could be selected many times, and data could reach the trap area. Furthermore, the employment of the introduced EVA-DF has a direct impact on reducing the energy consumption because the number of the candidate set is dynamically reduced, leading to avoiding the unreliable and void sensors joining the forwarding process. With increasing the number of nodes equal to 200, the energy consumption of EVA is reduced by about 14% and 42% compared to SPRE-PBR and VAPR. On the other hand, the energy consumption with a different number of sensors in VAPR and SPRE-PBR is dramatically increased with increasing the number of nodes. The reason for that is the VAPR void detection algorithm uses direction up/down technique, leading to select the sensors without considering the shortest path as some data packet needs to be forwarded to deeper sensors, and SPRE-PBR did not provide any solution for the communication void problem. These reasons cause high energy consumption. Figure 7 shows the effect of increasing the number of nodes on network lifetime in VAPR, SPRE-PBR, and EVA. When compared with other methods, the displayed results demonstrate that EVA has the best network lifetime. This is since route cost calculation uses minimum energy to optimize energy consumption and link quality, allowing the network lifetime to be maximized. Furthermore, the use of layering technique in the EVA-DF algorithm aids in the selection of the fastest distance while considering the route cost for each ordinary node, resulting in lower energy consumption and increased network lifetime. As a result, the data packets take the shortest path to the sink while balancing power usage. Consequently, the network lifetime has increased dramatically. Figure 6: Comparison of EVA with existing algorithms in terms of energy consumption. The network lifetime is decreasing with increasing numbers of sensors of VAPR and SPRE-PBR, as illustrated in Figure 7. In most cases, VAPR has high network life than SPRE-PBR and less than EVA. The reason is that VAPR uses 2-hop knowledge to pick the fastest route. However, the performance of finding the shortest route based on 2-hop knowledge is influenced by high-density networks, which reduces the lifetime of the network. In turn, EVA achieves stabilized network lifetime compared with other methods, with an expanding number of sensors. This is since the EVA-DF method may effectively work with any number of sensors by finding the shortest route while discarding node energy. Therefore, the energy is controlled by employing residual energy, and the hop count is minimized by stacking the forwarding region and improving link quality. As a result, the network lifetime is extended. Figure 8 depicts the effect of increasing the number of nodes in EVA on the packet delivery ratio, as well as a comparison to SPRE-PBR and VAPR. The results show that the packet delivery ratio in SPRE-PBR, VAPR, and EVA is increased with the growing number of sensors. In comparison to other techniques, EVA had the highest packet delivery ratio.