## 5 Performance Evaluation
### Comparative Analysis
#### 5.2.2 Efficiency of Data Gathering  
The efficiency of data gathering is the key idea that is addressed by the AUV. The AUV is enabled to predict the path and gather data from the employed sensors. The effectiveness of data gathering is estimated in terms of packet delivery ratio, end-to-end delay and collection delay. Packet delivery ratio is a significant metric that determines the success of data transmission via the selected route.  
Figure 10 indicates that the DEDG 3D-UWSN increases packet delivery ratio when compared with previous research works. This is due to the prediction of a route after validation by a gateway, and, hence, no packet is dropped or lost. The selection of neighbors in the route is required to be appropriate for the data transmission, else the packet delivery ratio is degraded. The previous ALP, bipartite K-means and ELMPP processes achieved 83%, 91% and 86%, respectively. Comparatively, AEC reached 95%, and DEDG 3D-UWSN reached 98% of packet delivery ratio. On the other hand, delay was also measured, since the data were gathered by the appointed AUV. The end-to-end delay defines the time taken to transmit the collected data from a CH to the AUV via intermediate hops. The collection delay depends on the time taken by the AUV to collect data from the set of sensors.  
Figure 10: Comparison of packet delivery ratios.  
Figure 9: Comparison of network lifetime.  
The performances of end-to-end delay and collection delay are illustrated in Figures 11 and 12, respectively. End-to-end delay is measured with respect to the number of hop counts and the collection delay with respect to the increase in the number of sensors in the network. The result shows that both delays were lower than in the previous works. This reduction is due to the efficient path prediction in the AUV and the inter-cluster routing, which validates the route using fuzzy-LeNet. In previous works, the AUV's path was planned and based on the data collected from sensors. The AUV collected sensed data only from a single sensor at a time, whereas, in the DEDG 3D-UWSN, the data are collected from four CHs at a time. Due to this, the AUV is enabled to collect data in the path faster, which reduces collection delay. In contrast, ELMPP has the highest collection delay, since it collects data from relay one after the other. The absence of clustering is also reflected in the increases in collection delay, since the data sensed by the sensor have to wait a longer time for the arrival of the AUV to collect their data.  
Figure 11: Comparison of end-to-end delay.  
Figure 12: Comparison of collection delay.  
The AUV tour length also impacts and increases the collection delay. If the tour length is longer, then the data collected by the sensor node have to hold on the packets until the AUV approaches near the sensor. Only the appropriate planning of the path in the AUV can minimize the tour length.  
In the previous works, the authors also discussed the assignment of a pre-defined path for data collection. Table 6 illustrates the comparison of the tour length of the AUV in each work. The tour length was reduced by 30-50 min in the proposed method compared to the AEC method. From this comparative analysis, the better efficiency of the proposed DEDG 3D-UWSN is illustrated, and the improvement in performance is due to the process handled in this work. The processes of clustering and scheduling aim to save energy in sensor nodes, and data gathering is performed with an AUV, the path of which is predicted effectively.