# W-GUN: Whale Optimization for Energy and Delay-Centric Green Underwater Networks
## 4 Results and Discussion
### Analysis of Results  
This section covers the comparative performance analysis part of the proposed approach with the existing methods. The analysis of the end-to-end delay versus the number of underwater sensor nodes is given in Figure 3. Here, the number of underwater sensor nodes is varied as 100, 200, 300, 400, and 500. It shows that the delay is considerably less for the proposed underwater relay optimization method W-GUN than the state-of-the-art techniques. It is evident that the optimal selection of underwater relay nodes enhances the performance of the overall network, resulting in reduced delay. For a lower delay, the underwater routing path should be as optimal as possible. The aim of the proposed optimization method is to select the maximal number of underwater routes with a minimal number of underwater relay nodes. Therefore, the route consists of the minimum number of underwater relay nodes in our proposed approach. Thus, the delay is considerably minimal compared with existing schemes in the underwater literature. A more detailed description of the end-to-end delay performance gain of the proposed framework is given in Table 1 with the comparative investigation of the frameworks HH-VBF, VBVA and ES-VBF, as described in the literature. It can be observed that the average performance gain of W-GUN in terms of percentage is 27%, 42%, and 50% for ES-VBF, VBVA and HH-VBF, respectively. This can be attributed to the fact that the natural underwater characteristics are not considered in the literature, which rather majorly relies on service quality and location-centric underwater relay nodes. However, W-GUN utilized underwater characteristics by the consideration of whales movement, resulting in a considerable performance gain. This performance gain is further represented in a more readable way in Figure 4, where percentage gain and end-to-end delay observations are shown to be in close relation. This verifies the results presented in Table 1 and Figure 3. Therefore, the proposed underwater framework outperforms the state-of-the-art techniques with considerably lower end-to-end delay. Figure 3: End-to-end delay versus the number of underwater nodes. The results in Figure 5 show the comparison of packet delivery ratio performance between the proposed framework and state-of-the-art techniques with varying underwater network density in the range of 100-500 sensor nodes. It can be observed that the packet delivery ratio is significantly higher for the proposed underwater relay optimization framework compared to state-of-the-art techniques. The better packet delivery ratio for the proposed framework can be attributed to the utilization of underwater environmental characteristics in relay node optimization, ultimately paying as a higher packet delivery rate. Furthermore, an underwater environmental scenario-centric delivery path is the target of the proposal's discovery mechanism, in order to have more stable nodes in the path for a higher packet delivery ratio. This means that the proposed underwater relay node optimization framework selects the more stable underwater routes with the optimal number of underwater relay nodes. Therefore, the delivery path consists of stable underwater relay nodes in our proposed approach. Thus, the packet delivery ratio is considerably higher compared with existing schemes in the underwater literature. A more detailed analysis of performance gain in terms of packet delivery ratio of the proposed framework is presented in Table 2. This comparative investigation considered the underwater state-of the-art literature for highlighting the respective performance gains against the framework in this study. It can be observed that the average performance gain of the proposal in terms of percentage is 15%, 23%, and 32% for ES-VBF, VBVA and HH-VBF, respectively. The reason behind this the utilization of underwater characteristics for identifying network dynamics which is not considered in the existing literature.