# Gathering Big Data in Wireless Sensor Networks by Drone +
## 7 Experiments
### Small Monitored Area  
Here, we consider Scenario 1 that is composed of 30 sensor nodes uniformly randomly deployed on a 2D square monitored area with 200 m of side. The drone flies at a constant speed of 2 m/s and hovers over the tour's hovering points. In the graph of Figure 11, we increased the amount of data stored in each sensor node and plotted these values on the X-axis. The Y-axis represents \(T_{total}\) obtained by the four heuristic methods. We verify that \(T_{total}\) increases when the amount of data in the sensor nodes increases, as expected. Incremental-Move outperformed Incremental in all scenarios, mainly in scenarios where the sensor nodes have more data to transmit. The same happens with Decremental-Move and Decremental. This shows that the proposed algorithm can effectively find sensor nodes able to send data to the drone in movement and, consequently, reduce the hovering time. Comparing only the heuristics that received the proposed algorithm, we verify that in scenarios when nodes are storing a smaller amount of data, Incremental-Move outperformed Decremental-Moved. However, in scenarios when nodes store a larger amount of data, Decremental-Moved presents the best results. This happens because the Decremental heuristic tends to find tours with more hovering points than the Incremental heuristic. Hence, the drone's path tends to be larger, and the number of sensor nodes able to send data during the drone movement increases.  
The graphs of Figures 12 and 13 help us to understand the behaviors presented by the methods in Figure 11. The time for data gathering (\(T_{total}\)) is the sum of the times spent by the drone flying to reach each hovering point (\(T_{trip}\)) and the time it hovers to gather data (\(T_{collecting}\)), according to Equation (1). Figure 12 shows \(T_{trip}\) only for Incremental and Decremental because Incremental-Move  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenario** & **Monitored Area (m\({}^{2}\))** & **Number of Sensor Nodes** & **Drone** & **Data in Each Sensor Node (kbits)** \\  & & & **Speed** & \\  & & **(m/s)** & & \\ \hline
1 & 200 & 30 & 2 & 20 to 120 \\
2 & 400 & 150 & 2 & 20 to 120 \\
3 & 400 & 100 to 250 & 2 & 60 \\
4 & 400 & 150 & 0.5 to 3.0 & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of each scenario.  
and Decremental-Move do not change the path created by these heuristics and followed by the drone. This graph shows that when increasing the amount of data stored in each sensor node memory the tour also increases, that is, the number of hovering points in the tour also increases. Figure 13 shows that \(T_{\textit{collecting}}\) grows when the amount of data in the sensor node memories increases. However, the growth of Incremental-Move and Decremental-Move is smaller than the growth of Incremental and Decremental. This is because the heuristics create longer tours when the nodes have more data to transmit, and the longer the tour is the larger will be the number of sensor nodes able to send data during the drone movement. Since the drone will receive data from more sensor nodes during its flight, it will reduce \(T_{\textit{collecting}}\), consequently also reducing \(T_{\textit{total}}\).  
These experiments show that the proposed algorithm for gathering data during the drone movement (Algorithm 3) can effectively reduce the overall data gathering time. Furthermore, it presents a better performance when the tour is longer and when the sensor nodes have more data to send to the drone.  
Figure 11: Overall data gathering time (\(T_{\textit{total}}\)) in a small monitored area.  
Figure 12: Drone moving time (\(T_{\textit{trip}}\)) in a small Monitored Area.