# UAV Path Optimization for Precision Agriculture Wireless Sensor Networks
## 5 Results
\begin{tabular}{c} Assume timeslots are assigned to each SN \\ Uses an algorithm called Watertank to manage SNs \\ active timeslots ensuring synchronization with \\ UAV path. Watertank ensures the closest SN \\ from UAV will be able to transmit its data, \\ reducing energy budget. \\ \end{tabular}  } & \multicolumn{2}{c}{
\begin{tabular}{c} Only natural landscapes (ievFigure 13 shows how many UAVs were necessary for each scenario during the reconfiguration phase. As we can see, there was a similar usage of UAVs for cases with 5 and 10 SNs, where the SN density is lower. Regarding scenarios \(S_{1}\) and \(S_{2}\), it is possible to notice the difference in the amount of UAVs for 15 and 20 SNs, due to the fact that they do not share timeslots, forcing the UAV to fly over greater distances. In scenario \(S_{3}\), where the UAV waits on each SN until it changes to active mode, we had a larger number of UAVs used for environments with 15 and 20 SNs. In scenario \(S_{4}\), where the UAV learning system is disabled, we can observe slightly higher values compared to \(S_{1}\) for 5 and 10 SNs. Finally, in the fifth scenario (\(S_{5}\)), we have a higher UAV occupancy compared to \(S_{1}\), \(S_{2}\), and \(S_{4}\), due to the reduced availability of the SN to a single timeslot. Figure 14 shows the average number of decisions taken by the UAVs for each scenario during the reconfiguration phase. A UAV decision can be to wait until the next timeslot and maintain its own position, or to go to another SN from its route list, due to restrictions for the calculated timeslot. In scenario \(S_{2}\), where the TSs are shared between SNs, the UAV makes more moves; thus, it acquires the desired knowledge faster when compared to the \(S_{1}\). In the third scenario, \(S_{3}\), the fact that the UAV needs to wait on a single SN until it changes to active mode makes it that the UAV does not acquire knowledge regarding other SNs at different timeslots. Therefore, the UAV is not able to use its knowledge base to make decisions to improve its route. Regarding \(S_{4}\), we opted to disable the Prohibition List strategy to measure the flight time and distance when compared to \(S_{1}\), so no decisions were made by the UAV. Finally, in \(S_{5}\), the reduction of the SN active time lowered the probability of \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{} & \multicolumn{3}{c}{**UAV’s Battery Consumption**} & \multicolumn{3}{c}{**Decisions**} & \multicolumn{3}{c}{**Flight Distance (km)**} & \multicolumn{3}{c}{**Flight Time (min)**} \\ \hline  & 5 & 10 & 15 & 20 & 5 & 10 & 15 & 20 & 5 & 10 & 15 & 20 & 5 & 10 & 15 & 20 \\ S\({}_{1}\) & 0.39 & 0.76 & 1.23 & 1.80 & 0.71 & 2.32 & 5.43 & 7.77 & 6.27 & 12.12 & 18.29 & 26.79 & 3.89 & 7.62 & 12.31 & 17.96 \\ S\({}_{2}\) & 0.54 & 1.64 & 2.85 & 3.83 & 0.64 & 3.25 & 7.30 & 10.38 & 6.76 & 13.80 & 23.36 & 35.47 & 5.45 & 16.36 & 28.50 & 38.31 \\ S\({}_{3}\) & 0.54 & 1.64 & 3.79 & 8.29 & 0.00 & 0.00 & 0.00 & 0.00 & 1.81 & 4.06 & 7.13 & 12.70 & 5.45 & 16.36 & 37.91 & 82.91 \\ S\({}_{4}\) & 0.77 & 1.71 & 2.04 & 4.62 & 0.00 & 0.00 & 0.00 & 0.00 & 8.02 & 16.10 & 20.65 & 32.18 & 7.65 & 17.11 & 20.44 & 46.16 \\ S\({}_{5}\) & 1.19 & 3.07 & 6.54 & 10.40 & 5.52 & 12.16 & 18.83 & 30.71 & 10.98 & 21.07 & 31.14 & 50.74 & 11.91 & 30.74 & 65.42 & 103.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Descriptive results obtained on simulations. Figure 13: Number of UAVs used in Simulation. the SN being found by the UAV; therefore, the UAV keeps continuously searching for SNs, flying over more targets, acquiring more knowledge, and making more decisions to change its own route. Figure 15 shows the average UAV flight distance for each scenario, during the SN reconfiguration phase. In \(S_{1}\) and \(S_{2}\), we can see close average values for 5 and 10 SNs. In \(S_{3}\), the UAV flies over each SN and hovers over it one by one. Thus, the flight pattern when compared to other scenarios is different.