# Cloud-Assisted UAV Data Collection for Multiple Emerging Events in Distributed WSNs
## 4 Simulation and Results
### Performance Comparisons  
_Flight Time and Distance_: To show the performance of the flight time and distance, we compare the results of our proposal with other methods: (1) the Full Collecting Method (FCM); (2) the event collection method which considers gathering all the data is called ECA; (3) the event collection method which considers the event priority is called ECP. Our approach, Cloud-assisted and Weight Event Data Collection, is called CWC. Figure 3 shows the assessment criteria of the flying time and the distance in Event-driven WSN (EDRW) using different methods. Experimenting with different numbers of events and clusters, we find that the flight distance of the CWC methods is much lower than the FCM, ECA, ECP methods. This is because, in the CWC method, calculating the shortest flying distance is one of the optimization objectives. In Figure 3a, we plot the results of 10 clusters with different event numbers. The flying distance is largest using the FCM approach. For ECA, ECP, and CWC, the flying distance gradually increases with the number of events. This is because the correlation cluster increases with the event number. Figure 3b plots the results of six event numbers with different cluster numbers. We see a similar trend in the flying distance for ECA, ECP, and CWC. In FCM, initially, the performance is better. However, the result gradually decreases and becomes worse after 10 clusters in EDRW. From the graphs, we can see that our approach outperforms the other strategies in terms of flying distance. Using our approach, a shorter flying time is used to collect event data from the ground WSN. Therefore, our proposal has the potential to reduce the energy consumption of the UAV. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Values** & **Parameters** & **Values** \\ \hline \(n\) & 5, 10, 15, 20, 25 & \(R_{\text{s}}\) & 40 m \\ \(m\) & 2, 4, 6, 8, 10 & \(R_{u}\) & 40 m \\ \(wj\) & 1–5 & \(H\) & 30 m \\ \(V_{n}\) & 250 kbps & \(V_{f}\) & 5 m/s \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The UAV and WSN parameters. _Integration of Data Acquisition:_ Figure 4 shows the converged data acquisition integrity of the wireless UAV in EDRW using the ECA, ECP, FCM, and CWC approaches. We can see that the data acquisition integrity in the CWC approach is higher than that in the ECA, ECP, and FCM approaches. The simulation results with 10 clusters and 6 events are shown in Figure 4a. The data acquisition integrity of the ECA, ECP, FCM and CWC approaches are 60%, 63%, 26%, and 97%, respectively. In the CWC approach, the UAV can stay over the WSN node for a time which is dependent on the data size, and gather information from the relevant nodes. In Figure 4b, the CWC has a high (greater than 92%) data acquisition integrity. In the ECA and ECP approaches, the values gradually decrease with an increase in the event number, as more sensor nodes and clusters become correlated with the events. Thus, an UAV must gather more data, which leads to the increasing in the amount of invalid information. However, the integrity of the data acquisition increases slightly, because with an increasing in the event number, FCM has a higher chance to collect valid data. From the graph, we can see that the CWC has a higher data acquisition integrity. Therefore, our proposal can gather more effective data for emerging events. Additionally, for the ground WSNs, the clusters would spend less energy and computational resources. This can prolong the work life of the WSN. _Total Flying Score:_ In Figure 5a, the total flying score of the ECA, ECP and CWC are plotted against different number of clusters. We can see that the CWC has a higher flying score than ECA and ECP. In the former, the ECA, ECP and CWC had slightly different scores when the numbers of clusters are low. However, with an increase in the number of clusters, the total flying score of CWC grows rapidly. This is because the CWC aims to optimize the flying distance, event priority, and cooperation with the ground WSN.